diff --git "a/LNAyT4oBgHgl3EQfgPhD/content/tmp_files/load_file.txt" "b/LNAyT4oBgHgl3EQfgPhD/content/tmp_files/load_file.txt" new file mode 100644--- /dev/null +++ "b/LNAyT4oBgHgl3EQfgPhD/content/tmp_files/load_file.txt" @@ -0,0 +1,1015 @@ +filepath=/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf,len=1014 +page_content='RiskProp: Account Risk Rating on Ethereum via De-anonymous Score and Network Propagation Dan Lin School of Software Engineering, Sun Yat-sen University Zhuhai, China lind8@mail2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='sysu.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='edu.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='cn Jiajing Wu∗ School of Computer Science and Engineering, Sun Yat-sen University Guangzhou, China wujiajing@mail.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='sysu.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='edu.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='cn Qishuang Fu School of Computer Science and Engineering, Sun Yat-sen University Guangzhou, China fuqsh6@mail2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='sysu.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='edu.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='cn Zibin Zheng School of Software Engineering, Sun Yat-sen University Zhuhai, China zhzibin@mail.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='sysu.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='edu.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='cn Ting Chen University of Electronic Science and Technology of China Guangzhou, China brokendragon@uestc.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='edu.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='cn ABSTRACT As one of the most popular blockchain platforms supporting smart contracts, Ethereum has caught the interest of both investors and criminals.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Differently from traditional financial scenarios, executing Know Your Customer verification on Ethereum is rather difficult due to the pseudonymous nature of the blockchain.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Fortunately, as the transaction records stored in the Ethereum blockchain are publicly accessible, we can understand the behavior of accounts or detect illicit activities via transaction mining.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Existing risk control techniques have primarily been developed from the perspectives of de-anonymizing address clustering and illicit account classification.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' However, these techniques cannot be used to ascertain the potential risks for all accounts and are limited by specific heuristic strate- gies or insufficient label information.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' These constraints motivate us to seek an effective rating method for quantifying the spread of risk in a transaction network.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' To the best of our knowledge, we are the first to address the problem of account risk rating on Ethereum by proposing a novel model called RiskProp, which in- cludes a de-anonymous score to measure transaction anonymity and a network propagation mechanism to formulate the relation- ships between accounts and transactions.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' We demonstrate the ef- fectiveness of RiskProp in overcoming the limitations of existing models by conducting experiments on real-world datasets from Ethereum.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Through case studies on the detected high-risk accounts, we demonstrate that the risk assessment by RiskProp can be used to provide warnings for investors and protect them from possible financial losses, and the superior performance of risk score-based account classification experiments further verifies the effectiveness of our rating method.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' KEYWORDS Abnormal detection, network propagation, Ethereum, risk control, de-anonymization 1 INTRODUCTION Ethereum [30] has the second-largest market cap in the blockchain ecosystem.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' The account model is adopted on Ethereum, and the native cryptocurrency on Ethereum is named Ether (abbreviated as ∗Corresponding author.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' “ETH”), which is widely accepted as payments and transferred from one account to another.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' It is known that Ethereum accounts are indexed according to pseudonyms, and the creation of accounts is almost cost-free.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' This anonymous nature and the lack of regulation result in the bad reputation of Ethereum and other blockchain sys- tems for breeding malicious behaviors and enabling fraud, thereby resulting in large property losses for investors.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' As reported in a Chainalysis Crime Report, the illicit share of all cryptocurrency activities was valued at nearly USD 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='7 billion in 2020.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' These losses illustrate that Know-Your-Customer (KYC) and risk control of ac- counts are critical and necessary.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Risk control [23] not only helps wallet customers identify risky accounts and avoid losses but also plays a vital role in the anti-money laundering of virtual asset service providers, such as cryptocurrency exchanges.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Therefore, a wealth of efforts have been expended in risk con- trol on Ethereum in recent years.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' In September 2020, the Financial Action Task Force (FATF) published a recommendation report on virtual assets and released information on Red Flag Indicators [11] related to transactions, anonymity, senders or recipients, the source of funds, and geographical risks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' In addition, researchers in the aca- demic community have proposed various techniques from the per- spectives of address clustering and illicit account classification.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Ad- dress clustering techniques perform entity identification of anony- mous accounts.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' For example, Victor [27] proposes several clustering heuristics for Ethereum accounts and clusters 17.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='9% of all active ex- ternally owned accounts.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Illicit account detection techniques focus on training classifiers based on well-designed features extracted from transactions [8, 10, 32].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Moreover, some researchers have de- veloped methods for automatic feature extraction incorporating structural information [19, 21, 29, 33].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' However, there are still some limitations (L) associated with these techniques.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' L1: Account clustering techniques can only be applied to part of accounts and therefore have limited applicability, and most accounts beyond heuristic rules cannot thus be identified.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' L2: In the existing methods for illicit account detection, binary classifiers are usually trained via supervised learning.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' However, as only a very small percentage of risky nodes have clear labels, which are required for these methods, the vast majority of accounts that may be involved in malicious events are unlabeled.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' In particular, arXiv:2301.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='00354v1 [cs.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='SI] 1 Jan 2023 WWW ’23, April 30–May 4, 2023, Austin, TX, US Anonymous author(s) TxnHash: 0x0bf742.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='..' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' From: 0x3da2b.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='..' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' To: 0x41b53.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='..' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Timestamp: 1525153486 Value: 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='1 Ether TxnFee: 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='000861 Ether Customer .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='..' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='..' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Scammer Scammer Exchange Txns Txns Txns .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='..' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Account risk rating Public transactions Ethereum blockchain 0x3da2b.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='..' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 0x41b53.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='..' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Figure 1: The procedure of ETH transfer in Ethereum.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' “From” denotes the sender, “To” denotes the receiver, and “Txn” denotes “Transaction”.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' risky accounts with few transactions or unseen patterns are likely to be misidentified in practical use.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' To address the limitations presented above, we explore risk con- trol on Ethereum from a new perspective: Account risk rating.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' In traditional financial scenarios, credit scoring is usually conducted by authorized financial institutions, which perform audits on their customers to fully understand their identity, background, and fi- nancial credit standing.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Similarly to credit scoring, risk rating on Ethereum can help us quantify the latent risk of a transaction or account with a quantitative score, thereby combating money laun- dering and identifying potential scams before new victims emerge.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' In terms of the abovementioned L1, in contrast to the traditional account clustering method, which can only de-anonymize a small number of accounts, the account risk method proposed in this paper can obtain quantitative risk indicators for all accounts.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Regarding L2, the proposed risk rating method can achieve decent perfor- mance in an unsupervised manner without feeding labels.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' The output of the proposed method is risk values, which are provided continuously and allow evaluation of the severity of risk.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Compared with traditional financial scenarios, several unique challenges (C) are encountered in the task of account risk rating on Ethereum.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' C1: Nature of anonymity.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Transactions on Ethereum do not require real-name verification.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Even worse, perpetrators of some malicious activities deliberately enhance their anonymity to counter the impact of de-anonymizing clustering techniques [29].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' C2: Complex transaction relationship.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Compared with tradi- tional financial scenarios, a user or entity on Ethereum may control a large number of accounts at almost no cost, and the transaction relationship between accounts is also more complex.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' How to quan- tify the impact of trading behavior between accounts on account risk is a challenging core problem.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' To overcome the challenges mentioned above, we propose a novel approach called Risk Propagation (RiskProp) for Ethereum ac- count rating.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' It comprises two core designs, namely de-anonymous score and a network propagation mechanism.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' To resolve C1, de- anonymous score measures the degree to which transactions remain anonymous.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' For example, both the payer and the payee of an illicit transaction prefer to have a small number of transactions to en- sure anonymity-preserving protection.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' In contrast, both sides of a licit transaction may participate in numerous interactions without evading the impact of the de-anonymized clustering algorithm.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Af- terward, to resolve C2, we model the massive transaction records as a directed bipartite graph and introduce a network propagation mechanism with three interdependent metrics, namely Confidence of the de-anonymous score, Trustiness of the payee, and Reliability of the payer.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Intuitively, payees with higher trustiness receive trans- actions with higher de-anonymous scores, and payers with higher reliability will send transactions with higher confidence.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Clearly, reliability, trustiness, and confidence are related to each other, so we define five items of prior knowledge that these metrics should satisfy and propose three mutually recursive equations to estimate the values of these metrics.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' To verify the effectiveness of the pro- posed risk rating method and further illustrate the significance of rating for risk control on Ethereum, we evaluate the effect of the risk rating system via experiments from two aspects, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=', analysis of risk rating results and rating score-based illicit/licit classification.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Overall, our contributions are summarized as follows: A new perspective for Ethereum risk control.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' This paper is the first to propose tackling the problem of Ethereum risk control via the perspective of account risk rating.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' A novel risk metric for transactions.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' We creatively develop a metric called de-anonymous score for transactions, which measures the degree of de-anonymization to quantify the risk of a transaction.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' An effective method and interesting insights.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' We implement a novel risk rating method called RiskProp and demonstrate its su- perior effectiveness and efficiency via experiments on a real-world Ethereum transaction dataset together with theoretical analysis.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' By analyzing the rating results and case studies on high-risk accounts, we obtain interesting insights into the Ethereum ecosystem and further show how our method could prevent financial losses ahead of blacklisting malicious accounts.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 2 PRELIMINARY 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='1 Ethereum Financial Background Ether is the native “currency” on Ethereum and plays a fundamen- tal part in the Ethereum payment system.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Ether can be paid or received in financial activities, just like currency in real life.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' In conventional financial scenarios, a Know Your Customer (KYC) check is the mandatory process to identify and verify a customer’s identity when opening an account and to periodically understand the legitimacy of the involved funds over time.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' However, unlike traditional transaction systems, where customers’ identity informa- tion is required and obtained in KYC checks, Ethereum accounts are designed as pseudonymous addresses identified by 20 bytes of public key information generated by cryptographic algorithms, for example, “0x99f154f6a393b088a7041f1f5d0a7cbfa795d301”.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Figure 1 depicts the risky scenario of Ether transfer in aspects of data acquisition.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' It includes three layers: 1) Ethereum blockchain.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' The Ethereum historical data are irreversible and publicly trace- able on the chain.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 2) Public transactions.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' The transaction denotes a signed data package from an account to another account, in- cluding the sending address, receiver address, transferred Ether amount, etc.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 3) Account risk rating.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Usually, the identities who con- trol the accounts are not labeled.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Customers may become involved in suspicious financial crimes or be vulnerable to frauds and scams.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Furthermore, the illicit funds can be laundered and cashed out via exchanges.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' In this procedure, our proposed RiskProp is implemented to measure the risk of unlabeled accounts that may have ill inten- tions and alert customers when engaging in suspicious, potentially illegal transactions.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' RiskProp WWW ’23, April 30–May 4, 2023, Austin, TX, US 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='2 The Nature of Blockchain: Anonymity It is known that the Ethereum account is identified as a pseudony- mous address.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' However, if customers repeatedly use the same ad- dress as on-chain identification, the relationship between addresses becomes linkable via public transaction records.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Accounts that participate in more transactions and connect with more accounts experience degrading anonymity [29].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' To reduce the likelihood of exposure, criminals naturally tend to initiate transactions with fewer accounts.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Here is an example on Ethereum: The two accounts of transaction 0x9a9d have only three transactions and became in- active thereafter.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' These two accounts are considered suspicious and reported as relevant accounts of Upbit exchange hack.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' On the con- trary, entities who do not deliberately take anonymity-preserving measures are likely to be normal [29].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Thus, the transaction is scored based on the fact of whether the accounts are trying to hide or not, which is the de-anonymous score.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Definition 1 (De-anonymous score, abbreviated as “score”).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' The de-anonymous score of a transaction from account𝑢 to 𝑣 where there is no intention to hide is defined as 𝑆𝑐𝑜𝑟𝑒(𝑢, 𝑣) =1 2 ( 2 log |𝑂𝑢𝑡𝑇𝑥𝑛(𝑢)| − log𝑚𝑎𝑥𝑂𝑢𝑡 log𝑚𝑎𝑥𝑂𝑢𝑡 + 2 log |𝐼𝑛𝑇𝑥𝑛(𝑣)| − log𝑚𝑎𝑥𝐼𝑛 log𝑚𝑎𝑥𝐼𝑛 ), (1) where 𝑂𝑢𝑡𝑇𝑥𝑛(𝑢) represents the outgoing transactions (payments) of payer 𝑢, 𝐼𝑛𝑇𝑥𝑛(𝑣) represents the incoming transactions (recep- tions) of payee 𝑣, and | × | denotes the size of a set.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' The minimum value of |𝑂𝑢𝑡𝑇𝑥𝑛(𝑢)| and |𝐼𝑛𝑇𝑥𝑛(𝑣)| is 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Let 𝑚𝑎𝑥𝑂𝑢𝑡 and 𝑚𝑎𝑥𝐼𝑛 be the largest number of payments and receptions, respectively.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' The de-anonymous scores of a transaction (𝑢, 𝑣) range from −1 (very high anonymity, abnormal) to 1 (very low anonymity, normal).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Intuitively, the score of (𝑢, 𝑣) increases as the transaction num- bers of either payer or payee grow.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Note that tricky criminals may camouflage themselves by deliberately conducting low-anonymity transactions [20].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='3 Transaction Network Construction First, each transaction on Ethereum has one payer (i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=', sender) and one payee (i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=', receiver).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Any account can be the role of payer or payee, just as a person in real life has different roles.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' The payee is a passive role and, therefore, we consider the incoming transactions to indicate the trustiness of an account.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' For instance, exchange accounts that receive more transactions are considered to be more trustworthy.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' In contrast, the payer is an active role and, thus, the outgoing transactions embody the intention of an account.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' For ex- ample, a scam account subjectively wants to transfer stolen money to its partners.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Next, the transaction records are modeled as a directed bipartite graph 𝐺 = (𝑈,𝑉,𝑆), where 𝑈 , 𝑉 , and 𝑆 represent the set of all payers, payees, and scores, respectively.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' A weighted edge (𝑢, 𝑣) denotes the transfer of Ethers from account 𝑢 ∈ 𝑈 to account 𝑣 ∈ 𝑉 with 𝑆𝑐𝑜𝑟𝑒(𝑢, 𝑣) ∈ 𝑆.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' The graph construction procedure is shown in Figure 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Then, the ego network of a payer 𝑢 is introduced.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' It is formed by its outgoing scores and corresponding payee neighbors, formulated Money transfer Accounts Tnx Score Accounts Payer Payee Tnx Score Accounts (A) Ethereum transaction records (B) De-anonymous score calculation (C) Payer-payee graph Figure 2: The transformation from the raw transaction records to the directed bipartite graph.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' “Txn” denotes “Transaction”.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Payee set V Payer A Payer B Payee X Payee Y Score(A, X) Score(B, Y) Payer set U Score(B, X) In( ) = { Score(A, X), Score(B, X) } Payee X Out( ) = { Score(B, X), Score(B, Y) } Payer B Figure 3: A toy example of the directed bipartite graph estab- lished from transactions and the illustration of functions 𝐼𝑛 and 𝑂𝑢𝑡.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' as 𝑂𝑢𝑡(𝑢) ∪ {𝑣|(𝑢, 𝑣) ∈ 𝑂𝑢𝑡(𝑢)}, where 𝑂𝑢𝑡(𝑢) is the set of scores connected with 𝑢.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' It is similar for the ego network of a payee, formulated as 𝐼𝑛(𝑣)∪{𝑢|(𝑢, 𝑣) ∈ 𝐼𝑛(𝑣)}.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Figure 3 shows an example in which there are two payers, two payees, and three transactions.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 3 MODEL In this section, we describe the prior knowledge that establishes the relationships among accounts and transactions and then propose risk propagation formulations that satisfy the prior knowledge.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' It is worth noticing that the proposed algorithm does not require handcraft feature engineering.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='1 Problem Definition and Model Overview Given raw transaction records of Ethereum, we model the transac- tion relationships between accounts as a directed bipartite graph 𝐺 = (𝑈,𝑉,𝑆) with payers and payees as nodes and prepossessed de-anonymous scores as weights of edges.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' We believe that accounts have intrinsic metrics to quantify their reliability and trustworthi- ness and transactions have intrinsic metrics to measure the con- fidence of their calculated de-anonymous scores.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Naturally, those metrics are interdependent and interplay with each other via the risk propagation mechanism: Payers vary in terms of their Reliability, which indicates how motivated they are.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' A licit payer without malicious intent usually does not hide himself or disguise its intentions during transactions.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Specifically, a reliable payer has harmless intentions regardless of whether it is transferring money to an exchange or to a scammer account (being gypped).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' In contrast, a perpetrator (e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='g.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=', a scammer) hopes to cover up its traces [27].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' The reliability metric 𝑅(𝑢) of a payer 𝑢 lies in [0, 1], ∀𝑢 ∈ 𝑈 .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' A value of 1 denotes a 100% reliable payer and 0 denotes a 0% reliable payer.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' WWW ’23, April 30–May 4, 2023, Austin, TX, US Anonymous author(s) Table 1: An example of propagation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑅0 is initial value, 𝑅𝑓 𝑖𝑛𝑎𝑙 and Risk𝑓 𝑖𝑛𝑎𝑙 are the results after convergence.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Account Label 𝑅0 𝑅𝑓 𝑖𝑛𝑎𝑙 Risk𝑓 𝑖𝑛𝑎𝑙 0xa768 Contract-deployer 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='7 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='8575 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='425 0x8271 Exchange 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='7 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='9526 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='474 0xebdc Phish-hack 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='7 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='1195 8.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='805 0xfe34 Phish-hack 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='7 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='2330 7.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='670 Payees vary in their trustworthiness level, measured by a metric called Trustiness, which indicates how trustworthy they are.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Intu- itively, a cryptocurrency service provider with a better reputation will receive more licit transactions (with higher scores) from well- motivated payers.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Trustiness of a payee 𝑇 (𝑣) ranges from 0 (very untrustworthy) to 1 (very trustworthy) ∀𝑣 ∈ 𝑉 .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' De-anonymous scores vary in terms of Confidence, which re- flects the confidence in the estimated risk probability of a trans- action.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' The confidence metric 𝐶𝑜𝑛𝑓 (𝑢, 𝑣) ranges from 0 (lack of confidence) to 1 (very confident).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' The connection between the reliability and risk of accounts: We define Reliability to characterize the risk rating of accounts because an account’s intention can be inferred by its (active) sending be- havior, rather than by its (passive) receiving behavior.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' A scammer transferring stolen money to its gang is a better reflection of its evil intention than the receipt of stolen money from victims.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' In the later section, we calculate the risk rating of accounts based on the Reliability of payer roles.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='2 Network Propagation Mechanism Given a cryptocurrency payer–payee graph, all intrinsic metrics are unknown but are interdependent.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Here, we introduce five items of prior knowledge that establish the relationships and how the net- work propagation mechanism is specially designed for our problem.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' The first two items of prior knowledge reflect the interdependency between a payee and the de-anonymous scores that they receive.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' [Prior knowledge 1] Payees with higher trustiness receive trans- actions with higher de-anonymous scores.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Intuitively, a payee receiving transactions with high de-anonymous scores is more likely to be trustworthy.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Formally, if two payees 𝑣1 and 𝑣2 have a one-to-one mapping, ℎ : 𝐼𝑛(𝑣1) → 𝐼𝑛(𝑣2) and 𝑆𝑐𝑜𝑟𝑒(𝑢, 𝑣1) > 𝑆𝑐𝑜𝑟𝑒(ℎ(𝑢), 𝑣2) ∀(𝑢, 𝑣1) ∈ 𝐼𝑛(𝑣1), then 𝑇 (𝑣1) > 𝑇 (𝑣2).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' [Prior knowledge 2] Payees with higher trustiness receive trans- actions with more positive confident scores.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' For two payees 𝑣1 and 𝑣2 with identical de-anonymous score networks, if the confidence of the in-transactions of payee 𝑣1 is higher than that of payee 𝑣2, the trustiness of payee 𝑣1 should be higher.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Formally, if two pay- ees 𝑣1 and 𝑣2 have a one-to-one mapping, ℎ : 𝐼𝑛(𝑣1) → 𝐼𝑛(𝑣2) and 𝐶𝑜𝑛𝑓 (𝑢, 𝑣1) > 𝐶𝑜𝑛𝑓 (ℎ(𝑢), 𝑣2) ∀(𝑢, 𝑣1) ∈ 𝐼𝑛(𝑣1), then 𝑇 (𝑣1) > 𝑇 (𝑣2).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' According to the above prior knowledge, we develop the Trusti- ness formulation for ∀𝑣 ∈ 𝑉 of our RiskProp algorithm: 𝑇 (𝑣) = � (𝑢,𝑣) ∈𝐼𝑛(𝑣) 𝑆𝑐𝑜𝑟𝑒(𝑢, 𝑣) × 𝐶𝑜𝑛𝑓 (𝑢, 𝑣) |𝐼𝑛(𝑣)| .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' (2) The next item of prior knowledge defines the relationship be- tween the score of a transaction and the connected payer–payee pair using the anonymous nature of cryptocurrency.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Algorithm 1 RiskProp Algorithm 1: Input: Directed Bipartite Graph 𝐺 = (𝑈,𝑉,𝑆) 2: Output: Risk of accounts 3: Initialize 𝑇 0 = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='5, 𝑅0 = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='7,𝐶𝑜𝑛𝑓 0 = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='5,𝑡 = 0, Δ = 1 4: while Δ ≥ 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='01 do 5: 𝑡 = 𝑡 + 1 6: Update 𝑡𝑟𝑢𝑠𝑡𝑖𝑛𝑒𝑠𝑠 of payees using Equation 2 7: Update 𝑟𝑒𝑙𝑖𝑎𝑏𝑙𝑖𝑡𝑦 of payers using Equation 4 8: Update 𝑐𝑜𝑛𝑓 𝑖𝑑𝑒𝑛𝑐𝑒 of transactions using Equation 3 9: Δ𝑇 = � 𝑣∈𝑉 |𝑇 𝑡 (𝑣) −𝑇 𝑡−1(𝑣) | 10: Δ𝑅 = � 𝑢∈𝑈 |𝑅𝑡 (𝑢) − 𝑅𝑡−1(𝑢) | 11: Δ𝐶 = � (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='𝑣)∈𝑆 |𝐶𝑜𝑛𝑓 𝑡 (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣) − 𝐶𝑜𝑛𝑓 𝑡−1(𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣) | 12: Δ = max{Δ𝑇 ,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Δ𝑅,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Δ𝐶 } 13: end while 14: 𝑅𝑖𝑠𝑘 (𝑢) = (1 − 𝑅(𝑢)) × 10,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' ∀𝑢 ∈ 𝑈 15: return [Prior knowledge 3] Confident de-anonymous scores of transac- tions are closely linked with the connected payee’s trustiness.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' For- mally, if two scores (𝑢1, 𝑣1) and (𝑢2, 𝑣2) are such that𝑆𝑐𝑜𝑟𝑒(𝑢1, 𝑣1) = 𝑆𝑐𝑜𝑟𝑒(𝑢2, 𝑣2),𝑅(𝑢1) = 𝑅(𝑢2), and |𝑆𝑐𝑜𝑟𝑒(𝑢1, 𝑣1) −𝑇 (𝑣1)| ⩽ |𝑆𝑐𝑜𝑟𝑒(𝑢2, 𝑣2) −𝑇 (𝑣2)|, then 𝐶𝑜𝑛𝑓 (𝑢1, 𝑣1) ⩾ 𝐶𝑜𝑛𝑓 (𝑢2, 𝑣2).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' We imply that different transactions sent by the same payers can have different intentions and anonymity.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Even scammers on Ethereum can have transactions that seem normal.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' [Prior knowledge 4] Transactions with higher confidence de- anonymous scores are sent by more reliable payers.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Formally, if two scores (𝑢1, 𝑣1) and (𝑢2, 𝑣2) are such that𝑆𝑐𝑜𝑟𝑒(𝑢1, 𝑣1) = 𝑆𝑐𝑜𝑟𝑒(𝑢2, 𝑣2), 𝑇 (𝑣1) = 𝑇 (𝑣2), and𝑅(𝑢1) ⩾ 𝑅(𝑢2), then𝐶𝑜𝑛𝑓 (𝑢1, 𝑣1) ⩾ 𝐶𝑜𝑛𝑓 (𝑢2, 𝑣2).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' This prior knowledge incorporates the payer’s intention in mea- suring the confidence of transaction scores.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' In this way, payees may have different confidence in receiving transactions with the same anonymity.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' For instance, exchanges on Ethereum receive funds from payers with different motivations—some are ordinary investors and some are suspicious accounts.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Below, we propose the Confidence formulation that satisfies the above items of prior knowledge: 𝐶𝑜𝑛𝑓 (𝑢, 𝑣) = 𝑅(𝑢) + (1 − |𝑆𝑐𝑜𝑟𝑒(𝑢, 𝑣) −𝑇 (𝑣)|) 2 .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' (3) Then, we describe how to quantify the Reliability metric of a payer by the transactions it sends.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' [Prior knowledge 5] Payers with higher reliability send transac- tions with higher confidence.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' For two payers 𝑢1 and 𝑢2 with equal scores, if payer 𝑢1 has higher confidence for all out transaction scores than𝑢2, then payer𝑢1 has a higher reliability.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Formally, if two payers 𝑢1 and 𝑢2 have ℎ : 𝑂𝑢𝑡(𝑢1) → 𝑂𝑢𝑡(𝑢2) and 𝐶𝑜𝑛𝑓 (𝑢1, 𝑣1) > 𝐶𝑜𝑛𝑓 (𝑢2,ℎ(𝑣)) ∀(𝑢1, 𝑣) ∈ 𝑂𝑢𝑡(𝑢1), then 𝑅(𝑢1) > 𝑅(𝑢2).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' The corre- sponding formulation of Reliability metric for ∀𝑢 ∈ 𝑈 is defined as 𝑅(𝑢) = � (𝑢,𝑣) ∈𝑂𝑢𝑡 (𝑢) 𝐶𝑜𝑛𝑓 (𝑢, 𝑣) |𝑂𝑢𝑡(𝑢)| .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' (4) Finally, the risk rating of an account is calculated by 𝑅𝑖𝑠𝑘(𝑢) = (1 − 𝑅(𝑢)) × 10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' The pseudo-code of RiskProp network propagation is described in Algorithm 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Let 𝑇 0, 𝐶𝑜𝑛𝑓 0, 𝑅0 be initial values and 𝑡 be the number of interactions.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' In the beginning, we have initial reliability 𝑅0 ∀𝑢 ∈ 𝑈 , initial trustiness 𝑇 0 = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='5 ∀𝑣 ∈ 𝑉 ,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' and RiskProp WWW ’23,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' April 30–May 4,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 2023,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Austin,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' TX,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' US Account Risk Rating Trustiness Confidence Risk Reliability Update Propagation Mechanism Risk Rating Results Analysis Ablation Study Risk Threshold Guarantees for Practice Comparative Evaluation Further Analysis Results Analysis Data Acquisition Labeled data Etherscan Ethereum Transactions Data Pre-processing Directed Bipartite Graph Construction De-anonymous Score Calculation Figure 4: The workflow of account risk rating on Ethereum.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' initial confidence 𝐶𝑜𝑛𝑓 0 = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='5 for all transactions.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Then, we keep updating metrics using Equations 2–4 until Δ is less than 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='01.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' RiskProp+: A Semi-supervised Version.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Sometimes, we have partial information about the labels of fraudulent accounts (verified, phishing scams, etc.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=') and licit accounts.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' We can take advantage of such prior information and incorporate them into our approach in a semi-supervised manner.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' In the semi-supervised RiskProp+, we initialize the Reliability metrics only for the training accounts.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' According to the risk levels of services reported by Chainalysis [26], we set 𝑅0 = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='9 for ICO wallet, Converter, and Mining, 𝑅0 = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='7 for Exchange, 𝑅0 = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='4 for Gambling, 𝑅0 = 0 for Phish/Hack, and set 𝑅0 = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='7 for testing accounts.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' The reliability values of labeled illicit accounts are unchanged during the training procedure.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Example.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Here, we use a small real-world dataset on Ethereum to intuitively show the results of RiskProp+ after interactions.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' We collect transactions of 10 accounts (6 for training and 4 for testing), including 28,598 accounts and 52,733 transactions in total.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Table 1 shows how the reliability of the 4 testing accounts varies over interactions (we omit trustiness and confidence for brevity).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' These testing accounts have the same reliability values at the beginning (𝑅0 = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='7).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' After convergence, accounts labeled as “phish/hack” get a lower value of reliability, and other licit accounts get higher reliability.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Confirming our intuition, RiskProp learns that accounts 0xebdc and 0xfe34 are high-risk accounts that investors need to be aware of.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Workflow for Account Risk Rating.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Figure 4 shows the work- flow of account risk rating on Ethereum, which contains four mod- ules: (i) Data acquisition collects accounts, transactions, and la- bels from Ethereum and Etherscan.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Only a few labels are provided, and these labels are not available in the unsupervised setting.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' (ii) Data pre-processing of raw transaction data described in Fig- ure 1 is conducted in two steps: de-anonymous score calculation and directed bipartite graph construction (i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=', payer–payee net- work).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' (iii) Account risk rating recursively calculates the Relia- bility, Trustiness of accounts, and Confidence of transaction scores until convergence, updated by the propagation mechanism.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' (iv) Re- sults analysis contains risk rating results analysis, comparative evaluation, and further analysis.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 4 EXPERIMENTS To investigate the effectiveness of RiskProp, we conduct experi- ments on a real-world Ethereum transaction dataset.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' As risk rating is an issue without any ground truth, we verify the effectiveness and significance of the risk rating results of RiskProp via three tasks: 1) risk rating analysis, which includes distribution of risk rating results and case studies of transaction pattern;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 2) comparative evaluation, which reports on the classification performance of labeled accounts compared with various baselines;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' and 3) further analysis, which contains ablation study, impact of risk threshold, and guarantees for practical use.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' RiskProp is open source and repro- ducible, and the code and dataset are publicly available after the paper is accepted.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='1 Data Collection We first obtain 803 ground truth account labels from an official Ethereum explorer and then include all the accounts and transac- tions that are within the one-hop and two-hop neighborhood of each labeled account.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Next, we filter out the zero-ETH transactions and construct the records into a graph, retaining the largest weakly connected component for experiments.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' As a result, there are 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='19 million accounts and 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='13 million transactions in the network.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' In the dataset, 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='02 percent (243) are labeled illicit (e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='g.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=', phishing scam), whereas 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='05 percent (560) are labeled licit (e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='g.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=', exchanges).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' The remaining unknown accounts are not labeled with regards to licit versus illicit.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='2 Effectiveness of De-anonymous Score We use one-way analysis of variance (ANOVA) to assess whether there is a significant difference between illicit and licit transactions in the proposed de-anonymous score in Equation (1).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' We consider a transaction as illicit (versus licit) if its payer is marked as illicit (versus licit).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Table 2 shows that compared with the random score, our proposed score achieves a larger mean square (MS) between groups and smaller MS within groups;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' in addition, our proposed score has a higher F value, and the 𝑝-value equals 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' These results suggest that the de-anonymous score is a useful metric for assessing the quality of transactions.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Table 2: ANOVA of random scores and de-anonymous scores.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Random scores De-anonymous score Src of var.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' MS F 𝑝-value MS F 𝑝-value Between groups 8.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='8 × 10−1 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='6 × 101 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='0 × 10−1 7.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='8 × 102 7.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='7 × 103 0 Within groups 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='3 × 10−1 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='0 × 10−1 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='3 Analysis of Risk Rating Results The principal task of RiskProp is to rate Ethereum accounts based on how ill-disposed they are.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Given the account risk rating obtained by RiskProp, we first review the results and investigate the capability of RiskProp in discovering new risky accounts.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Then, we dig deeper into the predicted high-risk accounts and obtain some insights.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='1 Distribution of risk rating results.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' The risk value of an account ranges from 0 (low risk) to 10 (high risk).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' The distribution of the predicted risk scores is as follows: 33.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='58% are located at (0,2], 63.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='45% WWW ’23, April 30–May 4, 2023, Austin, TX, US Anonymous author(s) (b) (a) (c) (d) Exchange (e) Phish_contract Victims Victims Exchange Scammer Scammers (f) 16 ETH 16 ETH 37 ETH 37 ETH 8 ETH 8 ETH 8 ETH Create 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='29 ETH 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='29 ETH Figure 5: Visualization showing some typical transaction patterns of risky accounts (in red circles).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' are located at (2,4], 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='03% are located at (4,6], 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='78% are located at (6, 8], and 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='19% are located at (8, 10].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' This is consistent with expectations: The risk value of the Ethereum transaction network meets the power distribution law, indicating that the overwhelming majority of accounts act normally, and only very few accounts have abnormal behaviors.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' We are interested in whether the high-risk accounts predicted by RiskProp are actually questionable.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Thus, we first manually check the top 150 accounts with the highest risk (with both in-coming and out-going transactions).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' The finding is that 119 out of 150 (approximately 80%) accounts have abnormal behaviors.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Among these 119 illicit accounts, 43 accounts are already labeled as “phish/hack” by Etherscan, whereas the remaining 76 are newly discovered suspicious accounts that are not marked in the existing label library.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' This result indicates the capabilities of RiskProp in predicting undiscovered risky accounts and reducing financial losses.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='2 Case studies of transaction pattern.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' We then manually veri- fied the predicted risky accounts by investigating their abnormal behaviors and find that there are many suspicious transaction pat- terns in the network.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' In order to save space, we show 6 typical patterns in Figure 5.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' These patterns are summarized from the real- world Ethereum transaction data and guided by current research and recommendation reports.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' (a) Hacking scammers are a list of addresses related to phish- ing and hacks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Figure 5(a) shows a pattern of phishing accounts reported by users who suffered financial loss.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' A typical phishing scam on Ethereum is the “Bee Token ICO Scam” attack, in which the phishers sent fake emails to the investors of an ICO with a fake Ethereum address to deposit their contributions into.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' For example, account 0xe336 has been confirmed to be part of this “Bee Token” scam, and 243 ETH has been sent to this address by 165 victims.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' (b) Fund source of hacking scammers are the upstream ac- counts of the known illicit accounts, which are collusion scam ac- counts to attract victims or provide money for hacking.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' As shown in Figure 5(b), the behaviors of collusion scam accounts may look similar to victims.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Nevertheless, we find that the upstream collusion accounts appear to participate in fewer transactions with shorter time intervals, and there are attempts to transfer the entire ETH balance of the scammers according to the Red Flag Indicators of FATF [11].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' (c) Money laundering of scammers are the downstream ac- counts of the known illicit accounts, which are collusion scam accounts to accept and transfer the stolen money, obfuscating the true sources.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' As shown in Figure 5(c), account 0x78f1 received stolen funds from several known hacking scammers, appearing to be the account used in the “placement” stage of money laun- dering.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Another example is 0xcfdd, which receives stolen funds from the Fake Starbase Crowdsale Contribution account 0x122c.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' (d) Zero-out middle accounts are the middle accounts that serve as a bridge defined by Li et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' [20].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' As shown in Figure 5(d), most of the received funds will be transferred out in short succession (such as within 24 hours).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' See 0x126e for an example.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' (e) Round transfers among exchanges denote a pattern that an account withdraws ETH without additional activity to a pri- vate wallet and then deposits back to the exchange, as shown in Figure 5(e).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Account 0x886e withdraws 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='4 ETH from Cryptopia exchange and then deposits the same amount of ETH back to Cryp- topia, which is an unnecessary step and incurs transaction fees [11].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Such a phenomenon indicates that the exchange is misused as a money-laundering mixer or is conducting wash trading [28].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' (f) Creators of illicit contracts are often the manipulators behind the scenes.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' The Origin Protocol phishing scam contact ac- count 0x9819 was created by account 0xff1a.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' After victims deposited money into the phishing contract, the creator transfers the stolen funds back to himself via internal transactions, which deliberately enhances anonymity.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' We observe that many illicit accounts are outside the label li- brary and are still considered risk-free.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Based on the results, we infer that our RiskProp is able to expose unlabeled illicit accounts.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' This is crucial on Ethereum, which lacks authorized and effective regulation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' In addition, the newly identified illicit accounts can complete the current label collection for additional analysis.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='4 Comparative Evaluation Settings To further evaluate the performance of our method and show the potential application, we employ the rating scores to conduct clas- sification experiments that divide Ethereum accounts into illicit and licit accounts, and we compare the results with the existing baseline methods for further verification.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' We wish to investigate if RiskProp can give a higher risk rating for the known illicit accounts and a lower rating for known licit accounts.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='1 Compared Methods.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' As mentioned earlier, RiskProp is the first algorithm that explores the risk rating of blockchain accounts.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' We chose a variety of methods (unsupervised and supervised) as baselines, which are similar to the problem we want to solve.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' We compare unsupervised RiskProp with (i) web page ranking, such as PageRank [6], and (ii) bipartite graph-based fraud detection, such as FraudEagle [2], BIRDNEST [12], and REV2 [16], which are also unsupervised methods.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' The (semi-)supervised approaches are as follows.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' (i) Machine learning methods, e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='g.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=', logistic regression (LR), naïve Bayes (NB), decision tree (DT), support vector machine (SVM), random for- est (RF), extreme gradient boosting (XGBoost), and LightGBM.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' These methods are used by [1, 4, 8, 19] for detection of abnor- mal Ethereum accounts.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' (ii) Traditional graph neural network, including DeepWalk, Node2Vec, and graph convolutional network RiskProp WWW ’23, April 30–May 4, 2023, Austin, TX, US 50 100 150 200 250 300 350 400 Top k 0 20 40 60 80 100 Precision@k (%) (a) 50 100 150 200 250 300 350 400 Top k 0 20 40 60 80 100 Recall@k (%) (b) RiskProp PageRank Birdnest FraudEagle REV2 Figure 6: The 𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛@𝑘 and 𝑅𝑒𝑐𝑎𝑙𝑙@𝑘 of illicit account pre- diction with different rating methods.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' (GCN) were conducted by Chen et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' [7] for detection of Ethereum phishing scams.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' (iii) Graph neural network for graphs with heterophily, such as CPGNN [34].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' The application of this type of algorithms is a recent research advancement in the task of Ethereum account classification [14].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='2 Evaluation Metrics.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' To evaluate the performance of the mod- els, we calculate the following metrics: 𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛,𝑅𝑒𝑐𝑎𝑙𝑙, 𝐹1,𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦, and 𝐴𝑈𝐶.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' As we know, there are only 6 out of 10,000 (0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='067 percent) accounts labeled in the entire dataset.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' To measure the order of the risk rating, we employ 𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛@𝑘 and 𝑅𝑒𝑐𝑎𝑙𝑙@𝑘 to evaluate the ranking order of the algorithm (@𝑘 means the top 𝑘 accounts).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' All baseline methods are tested using the original codes published by the authors.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' We repeat experiments 10 times and report the average results.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='3 Implementation Details.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' We evaluate the methods with bi- nary labeled accounts (illicit verse licit) and, thus, we assume ac- counts in the top 1% to be the illicit accounts (corresponding thresh- old: 6 for RiskProp).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' The reason for this threshold and percentage setting is discussed in Section 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='7.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' The split of the dataset in the (semi-)supervised setting is 𝑡𝑟𝑎𝑖𝑛𝑖𝑛𝑔 : 𝑡𝑒𝑠𝑡 = 8 : 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='5 Comparative Evaluation Results We report the 𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛@𝑘 and 𝑅𝑒𝑐𝑎𝑙𝑙@𝑘 curves of the compared algorithms, as shown in Figure 6.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' We observe that RiskProp obtains superior precision and recall than that of baseline with different 𝑘.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Up to 𝑘 = 100, the precision of RiskProp is almost 1 for illicit account prediction, which is surprising for an unsupervised setting.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' The 𝑅𝑒𝑐𝑎𝑙𝑙@𝑘 curve of RiskProp is significantly higher than the compared methods, and also increases steadily with 𝑘.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Table 3 shows the performance of unsupervised and supervised methods separately.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' We observe that RiskProp remarkably outperforms the unsupervised graph rating baselines in terms of accuracy and AUC, improving by 38.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='90% and 34.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='16%, respectively.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Meanwhile, for the licit account prediction, we observe that RiskProp beats the best baseline (FraudEagle) with a 10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='48% improvement in its F1-score.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' These demonstrate the effectiveness of our account risk rating method without labeling information.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Next, we turn our attention to the results of the semi-supervised RiskProp+ compared with the existing (semi-)supervised classifica- tion in Table 3, from which we derive the following conclusions: 1) RiskProp+ outperforms all baseline methods by 12.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='32% in terms of F1-score, 13.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='62% in terms of AUC, and 10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='56% in terms of accu- racy.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 2) The precision of licit accounts prediction is improved from Table 3: The classification results (%) of unsupervised and (semi-)supervised methods.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Illicit account Licit account Total Methods P R F1 P R F1 Acc.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' AUC PageRank 29.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='13 67.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='49 40.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='69 67.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='08 28.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='75 40.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='25 40.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='47 48.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='12 FraudEagle 12.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='28 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='88 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='670 68.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='36 91.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='07 78.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='10 64.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='38 46.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='98 BIRDNEST 22.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='24 47.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='32 30.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='26 55.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='24 28.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='21 37.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='35 34.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='00 37.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='77 REV2 14.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='10 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='527 6.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='854 68.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='00 88.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='04 76.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='73 62.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='76 46.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='28 RiskProp 71.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='48 71.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='48 76.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='15 91.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='44 85.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='89 88.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='58 84.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='56 83.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='69 Illicit account Licit account Total Methods P R F1 P R F1 Acc.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' AUC LR 65.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='67 74.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='58 69.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='84 83.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='87 77.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='23 80.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='41 76.' metadata={'source': 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'/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='84 81.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='82 84.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='26 76.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='88 71.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='68 RiskProp+ 70.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='91 84.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='78 77.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='23 93.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='33 85.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='96 89.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='49 85.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='63 85.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='37 Table 4: Illicit account prediction of ablation studies.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Methods Precision Recall F1-score RiskProp+ 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='7091 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='8478 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='7723 RiskProp+ (w/o label) 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='7148 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='8148 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='7615 RiskProp+ (w/o NP) 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='3811 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='9959 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='5513 RiskProp+ (w/o DS) 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='4737 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='1957 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='2769 82.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='52% (i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=', the average precision in baselines) to 93.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='33%, which means more licit accounts can be correctly identified.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 3) The supe- rior performance of RiskProp is more significant in the prediction of illicit accounts.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' The recall of illicit accounts prediction is improved from 60.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='56% (i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=', the average recall of illicit accounts prediction in baselines) to 84.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='78%.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' This shows the effectiveness of our framework in the prediction of both illicit and licit accounts.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='6 Ablation Study To further validate the contribution of each component of the pro- posed RiskProp+, we conduct an ablation study as follows.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' RiskProp+ (Full model): All components of the model and label data are included.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' w/o label: Labels are unavailable in the learning procedure, and the model is trained in an unsupervised manner.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' w/o network propagation (NP): Remove the NP procedure and calculate the average de-anonymous scores (𝐴𝐷𝑆) for each ac- counts’ outgoing transactions (payer role).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' An account is predicted as abnormal if its 𝐴𝐷𝑆 ⩽ 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' w/o de-anonymous score (DS): Replace DS with random scores, ranging from −1 to 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' We derive the following findings from Table 4: 1) Without the labels, the F1-score drops only slightly, indicating that our RiskProp does not rely on label data and can obtain good results in an un- supervised manner.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' To our surprise, the full model outperforms the RiskProp (w/o label), with a 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='3% increase in recall and 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='51% WWW ’23, April 30–May 4, 2023, Austin, TX, US Anonymous author(s) decrease in precision.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' This may be possibly explained by the re- liability values of labeled illicit accounts remaining unchanged during training in the supervised setting.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 2) RiskProp (w/o NP) has a only lower precision but a greatly improved recall, revealing that most of the illicit accounts are correctly predicted as illicit but that some licit accounts are misjudged to be illicit.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' This result demonstrates that de-anonymous score is an effective indicator of illicit transactions but their confidence varies among transactions.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' This result also confirms why we need to consider the confidence of the score in the propagation mechanism.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 3) RiskProp (w/o DS) yields low precision (47.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='37%) and severely low recall (19.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='57%).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' This result demonstrates that, even if the network propagation model is retained, the wrong scores of transactions will be spread through- out the entire Ethereum transaction network, resulting in poor prediction results.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='7 Risk Threshold of RiskProp Given accounts in the reversed order of their risk ratings, a natural question is how to classify licit or illicit accounts according to their risk ratings for the classification task?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' One possible option is to determine the percentage of known illicit labels in the dataset and set the risk value of this percentage as a demarcation line for account classification.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' However, the percentage is imprecise because some of the illicit accounts remain unrevealed according to the experimental results in Section 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Therefore, we try to establish a suitable risk threshold (𝑅𝑇𝐻) of RiskProp by conducting classification experiments.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Figure 7(a) demonstrates the results of illicit account prediction with different risk thresholds, ranging from 1 to 10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' As expected, the precision increases while the recall decreases with increasing 𝑅𝑇𝐻.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' In addition, F1, accuracy, and AUC first increase and then decrease with the increase in 𝑅𝑇𝐻.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' The best performance for F1 and AUC is when 𝑅𝑇𝐻 = 6.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Thus, we set the risk threshold 𝑅𝑇𝐻 = 6 for RiskProp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='8 Guarantees for Practical Use Here, we present guarantees for RiskProp in practical use regard- ing the following aspects: 1) guarantee of convergence;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 2) time complexity;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' and 3) linear scalability.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Convergence and Uniqueness.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' We present the theoretical prop- erties of RiskProp, including the proofs of prior knowledge, con- vergence, and uniqueness of the proposed metrics, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=', reliability, trustiness, and confidence.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Proofs are shown in the Appendix due to lack of space.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Time complexity.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' In each interaction, the RiskProp updates the reliability, Trustiness metrics of accounts and Confidence metric of transactions.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Therefore, the complexity of each iteration is O(|𝑈 | + |𝑆|) = O(|𝑆|), |𝑆| is the total edges in the payer–payee network.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Thus, for 𝑘 iterations, the total running time is O(𝑘|𝑆|).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Linear scalability.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' We have shown that RiskProp is linear in run- ning time in the number of nodes.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' To show this experimentally as well, we create random networks of an increasing number of nodes and edges and compute the running time of the algorithm until convergence.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Figure 7(b) shows that the running time increases lin- early with the number of nodes in the network.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Therefore, we can conclude that RiskProp is a scalable rating method that is suitable for applications on large-scale transaction networks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 1 2 3 4 5 6 7 8 9 10 RTH 0 20 40 60 80 100 Performance(%) Precision Recall F1 Accuracy AUC (a) Impact of different RTH 103 104 105 106 107 Number of nodes 10 1 100 101 102 103 104 Run time (seconds) (b) Scalability of RiskProp Figure 7: Further analysis of RiskProp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Analysis of incorrect predictions.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Furthermore, the results of the RiskProp+ experiment showed that 39 out of 46 (85%) phishing ac- counts were correctly predicted as illicit accounts.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' To understand why the remaining accounts failed to be detected as illicit by our model, we manually checked their transactions and neighbors and obtained the following results: (i) For one of the accounts, we have a risk score of 5.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='72, and in practice, the system will also warn about such accounts that are close to the risk threshold (𝑅𝑇𝐻 = 6).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' (ii) One account is set as the default risk value because the phishing account has no outgoing transactions for the time being, and in practice, we can make correct predictions as soon as the phish- ing account starts laundering money.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' (iii) Among the remaining five accounts, one account has a high transaction volume of 154.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' The remaining four accounts have a high volume of transactions along with withdrawals of ETH from exchanges, which directly contributed to the high de-anonymity score of transactions.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' How- ever, there are two sides to the story: regulation and fraud are a game of confrontation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' For hackers, reusing accounts reduces the probability of being identified as high risk and, at the same time, reusing accounts and withdrawing money from exchanges increase the risk of exposure and fund freezing.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 5 RELATED WORK Risk control studies in cryptocurrency.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' In recent years, there has been growing interest in account clustering and detecting il- licit activities (e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='g.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=', financial scams, money laundering) in cryp- tocurrency transaction networks [31].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Victor [27] is the first to propose clustering heuristics for the Ethereum’s account model, including deposit address reuse, airdrop multi-participation, and self-authorization.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' A recent review of the literature on cryptocur- rency scams [5] showed that the existing methods (e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='g.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=', [9], [10], and [15]) are mainly based on supervised classifiers fed with hand- crafted features.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Many attempts have been made [25, 29, 32] to incorporate structural information by learning the latent repre- sentations of accounts.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Some researchers have investigated and modeled the money flow from a network perspective [18, 21] to better identify illicit activities.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' After all, there is still a black area regarding the estimation of the risk value of Ethereum accounts, which is the key task in alerting about suspicious accounts and transactions on the chain.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Rating and ranking on graph data.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' The aim of ratings and rankings on graph data is to provide a score or an order for each node in a graph.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Currently, the main solutions are based on link analysis technique [24], Bayesian model [12], and iterative learn- ing [13], etc.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Similarly to the proposed RiskProp algorithm, [16] RiskProp WWW ’23, April 30–May 4, 2023, Austin, TX, US proposed axioms and iterative formulations to establish the rela- tionship between ratings.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' In [22], the authors measured the bias and prestige of nodes in networks based on trust scores.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' In [17], the authors highlighted that graph-based approaches provide unique solution opportunities for financial crime and fraud detection.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' A review on this topic [3] described the problems in current studies: lack of ground truths, imbalanced class, and large-scale network.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' These challenges also exist in our risk rating problem on Ethereum transaction networks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 6 CONCLUSIONS AND FUTURE WORK In this paper, we present the first systematic study to assess the account risk via a rating system named RiskProp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' In RiskProp, we modeled transaction records of Ethereum as a bipartite graph, pro- posed a novel metric called de-anonymous score to quantify the transaction risk, and designed a network propagation mechanism based on transaction semantics.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' By analyzing the rating results and manually checking the accounts with high risk, we evaluated the performance of RiskProp and obtained new insights about transac- tion risks on Ethereum.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' In addition, we employed the obtained risk scores to conduct illicit/licit account classification experiments on labeled data, and the superiority of this method over baseline meth- ods further verified the effectiveness of RiskProp in risk estimation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' For future work, we plan to integrate the transaction amounts and temporal information in our model, develop a web page or online tool for querying risk values of accounts, and share the details of risky cases with the Ethereum community.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' REFERENCES [1] Rachit Agarwal, Shikhar Barve, and Sandeep Kumar Shukla.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 2021.' metadata={'source': 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30–May 4, 2023, Austin, TX, US Anonymous author(s) A APPENDIX A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='1 Proof for Prior Knowledge In this part, we provide proofs that the proposed metrics, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=', Re- liability, Trustiness, and Confidence satisfy Prior knowledge 1 - 5.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Prior knowledge 1 in the main paper is the following: [Prior knowledge 1] Payees with higher trustiness receive trans- actions with higher de-anonymous scores.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Formally, if two pay- ees 𝑣1 and 𝑣2 have a one-to-one mapping, ℎ : 𝐼𝑛(𝑣1) → 𝐼𝑛(𝑣2) and 𝑆𝑐𝑜𝑟𝑒(𝑢, 𝑣1) > 𝑆𝑐𝑜𝑟𝑒(ℎ(𝑢), 𝑣2) ∀(𝑢, 𝑣1) ∈ 𝐼𝑛(𝑣1), then 𝑇 (𝑣1) > 𝑇 (𝑣2).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' The formulation to be used to show that the prior knowledge is satisfied is Equations 2, 3, and 4 in the main paper.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑇 (𝑣) = � (𝑢,𝑣)∈𝐼𝑛(𝑣) 𝑆𝑐𝑜𝑟𝑒 (𝑢, 𝑣) × 𝐶𝑜𝑛𝑓 (𝑢, 𝑣) |𝐼𝑛(𝑣) | 𝑅(𝑢) = � (𝑢,𝑣)∈𝑂𝑢𝑡 (𝑢) 𝐶𝑜𝑛𝑓 (𝑢, 𝑣) |𝑂𝑢𝑡 (𝑢) | 𝐶𝑜𝑛𝑓 (𝑢, 𝑣) = 𝑅(𝑢) + (1 − |𝑆𝑐𝑜𝑟𝑒 (𝑢, 𝑣) −𝑇 (𝑣) |) 2 Proof.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' To prove the Prior Knowledge 1, let us take two payees 𝑣1 and 𝑣2 that have identically ego networks and a one-to-one mapping ℎ, such that |𝐼𝑛(𝑣1)| = |𝐼𝑛(𝑣2)|, 𝐶𝑜𝑛𝑓 (𝑢, 𝑣1) = 𝐶𝑜𝑛𝑓 (ℎ(𝑢), 𝑣2), and 𝑆𝑐𝑜𝑟𝑒(𝑢, 𝑣1) > 𝑆𝑐𝑜𝑟𝑒(ℎ(𝑢), 𝑣2) ∀(𝑢, 𝑣1) ∈ 𝐼𝑛(𝑣1).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' According to Equation 2,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' we have 𝑇 (𝑣1) −𝑇 (𝑣2) = � (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='𝑣1)∈𝐼𝑛(𝑣1) 𝑆𝑐𝑜𝑟𝑒 (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣1) × 𝐶𝑜𝑛𝑓 (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣1) |𝐼𝑛(𝑣1) | − � (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='𝑣2)∈𝐼𝑛(𝑣2) 𝑆𝑐𝑜𝑟𝑒 (ℎ(𝑢),' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣2) × 𝐶𝑜𝑛𝑓 (ℎ(𝑢),' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' ���2) |𝐼𝑛(𝑣2) | = � (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='𝑣1)∈𝐼𝑛(𝑣1) (𝑆𝑐𝑜𝑟𝑒 (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣1) − 𝑆𝑐𝑜𝑟𝑒 (ℎ(𝑢),' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣2)) × 𝐶𝑜𝑛𝑓 (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣1) |𝐼𝑛(𝑣1) | As 𝑆𝑐𝑜𝑟𝑒(𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣1) > 𝑆𝑐𝑜𝑟𝑒(ℎ(𝑢),' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣2),' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' so 𝑇 (𝑣1) −𝑇 (𝑣2) > � (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='𝑣1)∈𝐼𝑛(𝑣1) 𝐶𝑜𝑛𝑓 (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣1) |𝐼𝑛(𝑣1) | As 𝐶𝑜𝑛𝑓 (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣1) ≥ 0 because Confidence are non-negative,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑇 (𝑣1) −𝑇 (𝑣2) > 0 ⇒ 𝑇 (𝑣1) > 𝑇 (𝑣2) The other items of prior knowledge have very similar and straight- forward proof.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' □ A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='2 Proof for Convergence Before the proof of convergence, we first discuss the boundary of proposed metrics.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' At the end of iteration 𝑡 of Algorithm 1, and by equation 2, 3, and 4, we get, 𝑇 𝑡 (𝑣) = � (𝑢,𝑣)∈𝐼𝑛(𝑣) 𝑆𝑐𝑜𝑟𝑒 (𝑢, 𝑣) × 𝐶𝑜𝑛𝑓 𝑡−1(𝑢, 𝑣) |𝐼𝑛(𝑣) | 𝑅𝑡 (𝑢) = � (𝑢,𝑣)∈𝑂𝑢𝑡 (𝑢) 𝐶𝑜𝑛𝑓 𝑡−1(𝑢, 𝑣) |𝑂𝑢𝑡 (𝑢) | 𝐶𝑜𝑛𝑓 𝑡 (𝑢, 𝑣) = 𝑅𝑡 (𝑢) + (1 − |𝑆𝑐𝑜𝑟𝑒 (𝑢, 𝑣) −𝑇 𝑡 (𝑣) | 2 ) 𝑇 ∞(𝑣), 𝑅(∞) (𝑢),𝐶𝑜𝑛𝑓 (∞) (𝑢, 𝑣) are their final values after con- vergence.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Lemma A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' (Boundary discussion) Set the maximum score in the transaction network as 𝑀, namely: 𝑀 = max (𝑢,𝑣) 𝑆𝑐𝑜𝑟𝑒 (𝑢, 𝑣) then |𝑀| < 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' The difference between a payee𝑣’s final Trustiness and its Trustiness after the first iteration is |𝑇 ∞(𝑢) −𝑇 1(𝑢) | ≤ |𝑀 | Similarly, |𝑅∞(𝑢) − 𝑅1(𝑢) | ≤ 1 |𝐶𝑜𝑛𝑓 ∞(𝑢, 𝑣) − 𝐶𝑜𝑛𝑓 1(𝑢, 𝑣) | ≤ 1 + |𝑀 | 2 = 𝛼 (𝛼 ≤ 1) Proof.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' First, we state that |𝑀| is strictly less than 1 in prac- tice.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' According to the formulation of 𝑆𝑐𝑜𝑟𝑒 of the main paper, we can see that 𝑆𝑐𝑜𝑟𝑒(𝑢, 𝑣) = 1 when 𝑂𝑢𝑡𝑇𝑥𝑛(𝑢) = 𝑚𝑎𝑥𝑂𝑢𝑡 and 𝐼𝑛𝑇𝑥𝑛(𝑣) = 𝑚𝑎𝑥𝐼𝑛, it is an extreme situation where the largest number of payments and receptions of the entire network appears in one transaction.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' The other case is 𝑆𝑐𝑜𝑟𝑒(𝑢, 𝑣) = −1 when that 𝑂𝑢𝑡𝑇𝑥𝑛(𝑢) = 1 and 𝐼𝑛𝑇𝑥𝑛(𝑣) = 1 at the same time.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' This situation presents to be some isolated transactions, however, they do not propagate risk and thus do not influence convergence.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' These situa- tions are out of our consideration.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' So we get |𝑀| is strictly smaller than 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Then,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' we prove that 𝑇 (𝑣) is bounded during the iterations: |𝑇 ∞(𝑣) −𝑇 1(𝑣) | = | � (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='𝑣)∈𝐼𝑛(𝑣) 𝑆𝑐𝑜𝑟𝑒 (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣) × 𝐶𝑜𝑛𝑓 ∞(𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣) |𝐼𝑛(𝑣) | − � (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='𝑣)∈𝐼𝑛(𝑣) 𝑆𝑐𝑜𝑟𝑒 (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣) × 𝐶𝑜𝑛𝑓 0(𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣) |𝐼𝑛(𝑣) | | Since |𝑥 + 𝑦| ≤ |𝑥| + |𝑦|,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' we get,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' |𝑇 ∞ (𝑣) −𝑇 1 (𝑣) | ≤ � (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='𝑣)∈𝐼𝑛(𝑣) |𝑆𝑐𝑜𝑟𝑒 (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣) × (𝐶𝑜𝑛𝑓 ∞ (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣) − 𝐶𝑜𝑛𝑓 0 (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣)) | |𝐼𝑛(𝑣) | Since |𝑥 × 𝑦| = |𝑥| × |𝑦|,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' we have,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' |𝑇 ∞ (𝑣) −𝑇 1 (𝑣) | ≤ � (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='𝑣)∈𝐼𝑛(𝑣) |𝑆𝑐𝑜𝑟𝑒 (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣) | × (𝐶𝑜𝑛𝑓 ∞ (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣) − 𝐶𝑜𝑛𝑓 0 (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣)) | |𝐼𝑛(𝑣) | (5) Since |𝑆𝑐𝑜𝑟𝑒(𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣)| ≤ |𝑀| ≤ 1,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' and |(𝐶𝑜𝑛𝑓 ∞(𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣)−𝐶𝑜𝑛𝑓 0(𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣))| ≤ 1,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' we get,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' |𝑇 ∞ (𝑣) −𝑇 1 (𝑣) | ≤ |𝑀 | × |𝐼𝑛(𝑣) | |𝐼𝑛(𝑣) | = |𝑀 | Next,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' we conduct the proof on 𝑅(𝑢): |𝑅∞ (𝑢) − 𝑅1 (𝑢) | = | � (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='𝑣)∈𝑂𝑢𝑡 (𝑢) 𝐶𝑜𝑛𝑓 ∞ (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣) − � (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='𝑣)∈𝑂𝑢𝑡 (𝑢) 𝐶𝑜𝑛𝑓 0 (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣) | |𝑂𝑢𝑡 (𝑢) | Again,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' since |𝑥 × 𝑦| = |𝑥| × |𝑦|,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' we get,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' |𝑅∞ (𝑢) − 𝑅1 (𝑢) | ≤ � (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='𝑣)∈𝑂𝑢𝑡 (𝑢) |𝐶𝑜𝑛𝑓 ∞ (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣) − 𝐶𝑜𝑛𝑓 0 (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣) | |𝑂𝑢𝑡 (𝑢) | (6) Similarly,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' since |(𝐶𝑜𝑛𝑓 ∞(𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣) − 𝐶𝑜𝑛𝑓 0(𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣))| ≤ 1,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' we have,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' |𝑅∞ (𝑢) − 𝑅1 (𝑢) | ≤ |𝑂𝑢𝑡 (𝑢) | |𝑂𝑢𝑡 (𝑢) | = 1 RiskProp WWW ’23,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' April 30–May 4,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 2023,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Austin,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' TX,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' US Finally,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' we calculate the bound of 𝐶𝑜𝑛𝑓 (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣): |𝐶𝑜𝑛𝑓 ∞ (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣) − 𝐶𝑜𝑛𝑓 1 (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣) | = |𝑅∞ (𝑢) − 𝑅1 (𝑢) + |𝑆𝑐𝑜𝑟𝑒 (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣) −𝑇 1 (𝑣) | − |𝑆𝑐𝑜𝑟𝑒 (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣) −𝑇 ∞ (𝑣) || 2 Since |𝑥 + 𝑦| ≤ |𝑥| + |𝑦|,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' we have |𝐶𝑜𝑛𝑓 ∞ (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣) − 𝐶𝑜𝑛𝑓 1 (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣) | ≤ |𝑅∞ (𝑢) − 𝑅1 (𝑢) | + ( ||𝑆𝑐𝑜𝑟𝑒 (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣) −𝑇 1 (𝑣) | − |𝑆𝑐𝑜𝑟𝑒 (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣) −𝑇 ∞ (𝑣) ||) 2 Since ||𝑥| − |𝑦|| ≤ |𝑥 − 𝑦|,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' it follows that,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' |𝐶𝑜𝑛𝑓 ∞ (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣) − 𝐶𝑜𝑛𝑓 1 (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣) | ≤ |𝑅∞ (𝑢) − 𝑅1 (𝑢) | + |𝑇 ∞ (𝑣) −𝑇 1 (𝑣) | 2 (7) Since |𝑅∞(𝑢) − 𝑅1(𝑢)| ≤ 1,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' and|𝑇 ∞(𝑣) −𝑇 1(𝑣)| ≤ |𝑀|,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' we get,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' |𝐶𝑜𝑛𝑓 ∞ (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣) − 𝐶𝑜𝑛𝑓 1 (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣) | ≤ 1 + |𝑀 | 2 For convenience,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' we let 1+|𝑀 | 2 = 𝛼.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Since |𝑀| < 1, then 𝛼 < 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' □ Theorem A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Convergence of Propagation: The difference during iterations is bounded as as |𝐶𝑜𝑛𝑓 ∞(𝑢, 𝑣) − 𝐶𝑜𝑛𝑓 𝑡 (𝑢, 𝑣)| ≤ 𝛼𝑡 (𝛼 = 1+|𝑀 | 2 < 1), ∀(𝑢, 𝑣) ∈ 𝑆.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' As 𝑡 increases, the difference decreases and 𝐶𝑜𝑛𝑓 𝑡 (𝑢, 𝑣) converges to |𝐶𝑜𝑛𝑓 ∞(𝑢, 𝑣).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Similarly, |𝑇 ∞(𝑣) −𝑇𝑡 (𝑣)| ≤ 𝛼𝑡−1, ∀𝑣 ∈ 𝑉 , |𝑅∞(𝑢) − 𝑅𝑡 (𝑢)| ≤ 𝛼𝑡−1, ∀𝑢 ∈ 𝑈 .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Proof.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Similar to Equations 5, 6, and 7, we have, |𝐶𝑜𝑛𝑓 ∞ (𝑢, 𝑣) − 𝐶𝑜𝑛𝑓 𝑡 (𝑢, 𝑣) | ≤ |𝑅∞ (𝑢) − 𝑅𝑡 (𝑢) | + |𝑇 ∞ (𝑣) −𝑇𝑡 (𝑣) | 2 (8) |𝑅∞ (𝑢) − 𝑅𝑡 (𝑢) | ≤ � (𝑢,𝑣,)∈𝑂𝑢𝑡 (𝑢) |𝐶𝑜𝑛𝑓 ∞ (𝑢, 𝑣) − 𝐶𝑜𝑛𝑓 𝑡−1 (𝑢, 𝑣) | |𝑂𝑢𝑡 (𝑢) | (9) |𝑇 ∞ (𝑣) −𝑇𝑡 (𝑣) | ≤ � (𝑢,𝑣)∈𝐼𝑛(𝑣) |𝑆𝑐𝑜𝑟𝑒 (𝑢, 𝑣) | × |(𝐶𝑜𝑛𝑓 ∞ (𝑢, 𝑣) − 𝐶𝑜𝑛𝑓 𝑡−1 (𝑢, 𝑣)) | |𝐼𝑛(𝑣) | (10) First, we will prove the convergence of Confidence using mathe- matical induction.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Base case of induction.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' When 𝑡 = 1, as we proved in Lemma A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='1, we get: |𝐶𝑜𝑛𝑓 ∞(𝑢, 𝑣) − 𝐶𝑜𝑛𝑓 1(𝑢, 𝑣) | ≤ 𝛼1 Induction step.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' We assume by hypothesis that |𝐶𝑜𝑛𝑓 ∞(𝑢, 𝑣) − 𝐶𝑜𝑛𝑓 𝑡−1(𝑢, 𝑣) | ≤ 𝛼𝑡−1, which is consistent with the base case already.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Then,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' by substituting Equations 9 and 10 into Equation 8,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' for the case in the next iteration where time is 𝑡,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' we have,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' |𝐶𝑜𝑛𝑓 ∞ (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣) − 𝐶𝑜𝑛𝑓 𝑡 (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣) | ≤ � (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='𝑣)∈𝑂𝑢𝑡 (𝑢) |𝐶𝑜𝑛𝑓 ∞ (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣) − 𝐶𝑜𝑛𝑓 𝑡−1 (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣) | 2 × |𝑂𝑢𝑡 (𝑢) | ) + � (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='𝑣)∈𝐼𝑛(𝑣) |𝑆𝑐𝑜𝑟𝑒 (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣) | × |(𝐶𝑜𝑛𝑓 ∞ (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣) − 𝐶𝑜𝑛𝑓 𝑡−1 (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣) | 2 × |𝐼𝑛(𝑣) | ≤ 1 2 × � ( 1 + |𝑀 | 2 )𝑡−1 + |𝑀 | × � (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='𝑣)∈𝐼𝑛(𝑣) |𝐶𝑜𝑛𝑓 ∞ (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣) − 𝐶𝑜𝑛𝑓 𝑡−1 (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣) | |𝐼𝑛(𝑣) | � ≤ 1 2 × � ( 1 + |𝑀 | 2 )𝑡−1 + |𝑀 | × ( 1 + |𝑀 | 2 )𝑡−1 � ≤ ( 1 + |𝑀 | 2 )𝑡 = 𝛼𝑡 Therefore,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' |𝐶𝑜𝑛𝑓 ∞(𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣) − 𝐶𝑜𝑛𝑓 𝑡 (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣)| ≤ 𝛼𝑡.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' |𝑅∞ (𝑢) − 𝑅𝑡 (𝑢) | ≤ � (𝑢,𝑣)∈𝑂𝑢𝑡 (𝑢) |𝐶𝑜𝑛𝑓 ∞ (𝑢, 𝑣) − 𝐶𝑜𝑛𝑓 𝑡−1 (𝑢, 𝑣) | |𝑂𝑢𝑡 (𝑢) | ≤ � (𝑢,𝑣)∈𝑂𝑢𝑡 (𝑢) ( 1+|𝑀| 2 )𝑡−1 |𝑂𝑢𝑡 (𝑢) | ≤ ( 1 + |𝑀 | 2 )𝑡−1 = 𝛼𝑡−1 |𝑇 ∞ (𝑣) −𝑇 (𝑣)𝑡 | ≤ � (𝑢,𝑣)∈𝐼𝑛(𝑣) |𝑆𝑐𝑜𝑟𝑒 (𝑢, 𝑣) | × |(𝐶𝑜𝑛𝑓 ∞ (𝑢, 𝑣) − 𝐶𝑜𝑛𝑓 𝑡−1 (𝑢, 𝑣)) | |𝐼𝑛(𝑣) | ≤ � (𝑢,𝑣)∈𝐼𝑛(𝑣) |(𝐶𝑜𝑛𝑓 ∞ (𝑢, 𝑣) − 𝐶𝑜𝑛𝑓 𝑡−1 (𝑢, 𝑣)) | |𝐼𝑛(𝑣) | ≤ � (𝑢,𝑣)∈𝐼𝑛(𝑣) ( 1+|𝑀| 2 )𝑡−1 |𝐼𝑛(𝑣) | ≤ ( 1 + |𝑀 | 2 )𝑡−1 = 𝛼𝑡−1 As discussed in the Lemma A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' we know that |𝑀| is strictly smaller than 1, then we have 𝛼 < 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' As 𝑡 increases, 𝛼𝑡−1 → 0 and 𝛼𝑡 → 0, so after t iterations, 𝐶𝑜𝑛𝑓 (𝑢, 𝑣)𝑡 → 𝐶𝑜𝑛𝑓 ∞(𝑢, 𝑣), 𝑅(𝑢)𝑡 → 𝑅∞(𝑢), and 𝑇 (𝑣)𝑡 → 𝑇 ∞(𝑣), the algorithm converges.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' □ A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='3 Proof for Uniqueness In this part, we provides proofs that Reliability, Trustiness, and Confidence are unique.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Theorem A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Confidence, Reliability, and Trustiness converge to the unique value.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Proof.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' First, we consider the uniqueness of Confidence using mathematical contradiction.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Let the 𝐶𝑜𝑛𝑓 (𝑢, 𝑣) converges to different values.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' So, let (𝑢, 𝑣) be the transaction with maximum Confidence difference, 𝐷 (with 𝐷 ≥ 0), between its two possible 𝐶𝑜𝑛𝑓1(𝑢, 𝑣) and 𝐶𝑜𝑛𝑓2(𝑢, 𝑣).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' According to Equation 8,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' we get,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝐷 = |𝐶𝑜𝑛𝑓 ∞ 1 (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣) − 𝐶𝑜𝑛𝑓 ∞ 2 (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣) | ≤ |𝑅∞ 1 (𝑢) − 𝑅∞ 2 (𝑢) | + |𝑇 ∞ 1 (𝑣) −𝑇 ∞ 2 (𝑣) | 2 (11) Then,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' according to Equation 9 and 10,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' we have,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' |𝑅∞ 1 (𝑢) − 𝑅∞ 2 (𝑢) | ≤ � (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='𝑣)∈𝑂𝑢𝑡 (𝑢) |𝐶𝑜𝑛𝑓 ∞ 1 (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣) − 𝐶𝑜𝑛𝑓 ∞ 2 (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣) | |𝑂𝑢𝑡 (𝑢) | ≤ 𝐷 (12) WWW ’23,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' April 30–May 4,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 2023,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Austin,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' TX,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' US Anonymous author(s) |𝑇 ∞ 1 (𝑣) −𝑇 ∞ 2 (𝑣) | ≤ � (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='𝑣)∈𝐼𝑛(𝑣) |𝑆𝑐𝑜𝑟𝑒 (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣) | × |𝐶𝑜𝑛𝑓 ∞ 1 (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣) − 𝐶𝑜𝑛𝑓 ∞ 2 (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣) | |𝐼𝑛(𝑣) | ≤ |𝑀 | × 𝐷 (13) We substitute Equation 12 and 13 into Equation (11),' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' and get,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝐷 = |𝐶𝑜𝑛𝑓 ∞ 1 (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣) − 𝐶𝑜𝑛𝑓 ∞ 2 (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣) | ≤ 1 2 × ( � (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='𝑣)∈𝑂𝑢𝑡 (𝑢) |𝐶𝑜𝑛𝑓 ∞ 1 (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣) − 𝐶𝑜𝑛𝑓 ∞ 2 (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣) | |𝑂𝑢𝑡 (𝑢) | + � (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='𝑣)∈𝐼𝑛(𝑣) |𝑆𝑐𝑜𝑟𝑒 (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣) | × |𝐶𝑜𝑛𝑓 ∞ 1 (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣) − 𝐶𝑜𝑛𝑓 ∞ 2 (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣) | |𝐼𝑛(𝑣) | ≤ 1 2 × � 𝐷 + |𝑀 | × � (𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content='𝑣)∈𝐼𝑛(𝑣) |𝐶𝑜𝑛𝑓 ∞(𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣) − 𝐶𝑜𝑛𝑓 ∞(𝑢,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' 𝑣) | |𝐼𝑛(𝑣) | � ≤ 1 2 × (𝐷 + |𝑀 | × 𝐷) ≤ ( 1 + |𝑀 | 2 ) × 𝐷 = 𝛼 × 𝐷 Thus,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' by solving 𝐷 ≤ 𝛼 × 𝐷(𝛼 ≠ 0) and with the condition that 𝐷 ≥ 0,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' we obtain 𝐷 = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' Then, |𝐶𝑜𝑛𝑓 ∞ 1 (𝑢, 𝑣) − 𝐶𝑜𝑛𝑓 ∞ 2 (𝑢, 𝑣)| = 0 and converge value of Confidence is unique.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' The uniqueness of Trustiness and Reliability have similar proof.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'} +page_content=' □' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/LNAyT4oBgHgl3EQfgPhD/content/2301.00354v1.pdf'}