url: =Paper Digest: WWW 2021 Highlights – Paper Digest\nPaper Digest: WWW 2021 Highlights\nJune 24, 2021 August 24, 2021 admin\nDownload WWW-2021-Paper-Digests.pdf– highlights of all WWW-2021 papers. Readers can also choose to read this highlight article on our console, which allows users to filter out papers using keywords. The Web Conference (WWW) is one of the top internet conferences in the world. In 2021, it is to be held online.\nTABLE 1: Paper Digest: WWW 2021 Highlights\n1 REST: Relational Event-driven Stock Trend Forecasting\nHighlight: In this paper, we propose a relational event-driven stock trend forecasting (REST) framework, which can address the shortcoming of existing methods. Wentao Xu; Weiqing Liu; Chang Xu; Jiang Bian; Jian Yin; Tie-Yan Liu;\n2 Exploring The Scale-Free Nature of Stock Markets: Hyperbolic Graph Learning for Algorithmic Trading\nHighlight: To capture the scale-free spatial and temporal dependencies in stock prices, we propose HyperStockGAT: Hyperbolic Stock Graph Attention Network, the first model on the Riemannian Manifolds for stock selection. Ramit Sawhney; Shivam Agarwal; Arnav Wadhwa; Rajiv Shah;\n3 Detecting and Quantifying Wash Trading on Decentralized Cryptocurrency Exchanges\nHighlight: In this paper, we illustrate how wash trading activity can be identified on two of the first popular limit order book-based decentralized exchanges on the Ethereum blockchain, IDEX and EtherDelta. Friedhelm Victor; Andrea Marie Weintraud;\n4 Towards Understanding and Demystifying Bitcoin Mixing Services\nHighlight: Specifically, we propose a generic abstraction model for mixing services and observe that there are two mixing mechanisms in the wild, i.e. swapping and obfuscating. Lei Wu; Yufeng Hu; Yajin Zhou; Haoyu Wang; Xiapu Luo; Zhi Wang; Fan Zhang; Kui Ren;\n5 Towards Understanding Cryptocurrency Derivatives:A Case Study of BitMEX\nHighlight: In this paper we explore this new paradigm through a study of BitMEX, one of the first and most successful derivatives platforms for leveraged cryptocurrency trading. Kyle Soska; Jin-Dong Dong; Alex Khodaverdian; Ariel Zetlin-Jones; Bryan Routledge; Nicolas Christin;\n6 On The Feasibility of Automated Built-in Function Modeling for PHP Symbolic Execution\nHighlight: In this paper, we explore the feasibility of automating the process of modeling PHP built-in functions for symbolic execution. Penghui Li; Wei Meng; Kangjie Lu; Changhua Luo;\n7 TLS 1.3 in Practice:How TLS 1.3 Contributes to The Internet\nHighlight: In this paper, we take a closer look at TLS 1.3 deployments in practice regarding adoption rate, security, performance, and implementation by applying temporal, spatial, and platform-based approaches on 687M connections. Hyunwoo Lee; Doowon Kim; Yonghwi Kwon;\n8 Towards Realistic and ReproducibleWeb Crawl Measurements\nHighlight: Our work improves the state of web privacy and security by investigating how key measurements differ when using naive crawling tool defaults vs. careful attempts to match “real” users across the Tranco top 25k web domains. Jordan Jueckstock; Shaown Sarker; Peter Snyder; Aidan Beggs; Panagiotis Papadopoulos; Matteo Varvello; Benjamin Livshits; Alexandros Kapravelos;\n9 #Twiti: Social Listening for Threat Intelligence\nHighlight: In this paper, we present Twiti, a system that automatically extracts various forms of malware IOCs from Twitter. Hyejin Shin; WooChul Shim; Saebom Kim; Sol Lee; Yong Goo Kang; Yong Ho Hwang;\n10 An Investigation of Identity-Account Inconsistency in Single Sign-On\nHighlight: In this paper, we investigate the identity-account inconsistency threat, a new SSO vulnerability that can cause the compromise of online accounts. Guannan Liu; Xing Gao; Haining Wang;\n11 An Alternative Cross Entropy Loss for Learning-to-Rank\nHighlight: In this work, we propose a cross entropy-based learning-to-rank loss function that is theoretically sound, is a convex bound on NDCG—a popular ranking metric—and is consistent with NDCG under learning scenarios common in information retrieval. Sebastian Bruch;\n12 Diversification-Aware Learning to Rank Using Distributed Representation\nHighlight: In this paper, we propose a soft version of the “next document” paradigm in which we associate each document with an approximate rank, and thus the subtopics covered prior to a document can also be estimated. Le Yan; Zhen Qin; Rama Kumar Pasumarthi; Xuanhui Wang; Michael Bendersky;\n13 Maximizing Marginal Fairness for Dynamic Learning to Rank\nHighlight: To address this problem, we propose a fair and unbiased ranking method named Maximal Marginal Fairness (MMF). Tao Yang; Qingyao Ai;\n14 PairRank: Online Pairwise Learning to Rank By Divide-and-Conquer\nHighlight: In this work, we propose to estimate a pairwise learning to rank model online. Yiling Jia; Huazheng Wang; Stephen Guo; Hongning Wang;\n15 Robust Generalization and Safe Query-Specializationin Counterfactual Learning to Rank\nHighlight: We introduce the Generalization and Specialization (GENSPEC) algorithm, a robust feature-based counterfactual LTR method that pursues per-query memorization when it is safe to do so. Harrie Oosterhuis; Maarten de de Rijke;\n16 Communication Efficient Federated Generalized Tensor Factorization for Collaborative Health Data Analytics\nHighlight: We propose a communication efficient federated generalized tensor factorization, which is flexible enough to choose from a variate of losses to best suit different types of data in practice. Jing Ma; Qiuchen Zhang; Jian Lou; Li Xiong; Joyce C. Ho;\n17 Completing Missing Prevalence Rates for Multiple Chronic Diseases By Jointly Leveraging Both Intra- and Inter-Disease Population Health Data Correlations\nHighlight: To tackle this problem, this paper proposes a deep-learning-based approach, called Compressive Population Health (CPH), to infer and recover (to complete) the missing prevalence rate entries of multiple chronic diseases. Yujie Feng; Jiangtao Wang; Yasha Wang; Sumi Helal;\n18 Towards Facilitating Empathic Conversations in Online Mental Health Support: A Reinforcement Learning Approach\n19 Search Engines Vs. Symptom Checkers: A Comparison of Their Effectiveness for Online Health Advice\nHighlight: To this end, we conducted a user study with 64 real-world users performing 8 simulated self-diagnosis tasks. Sebastian Cross; Ahmed Mourad; Guido Zuccon; Bevan Koopman;\n20 UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced Data\nHighlight: To fill the gap, we propose UNcertaInTy-based hEalth risk prediction (UNITE) model. Chacha Chen; Junjie Liang; Fenglong Ma; Lucas Glass; Jimeng Sun; Cao Xiao;\n21 MIRA:Leveraging Multi-Intention Co-click Information in Web-scale Document Retrieval Using Deep Neural Networks\nHighlight: In this work we aim to leverage the additional information for documents from their co-click neighbours to help document retrieval. Yusi Zhang; Chuanjie Liu; Angen Luo; Hui Xue; Xuan Shan; Yuxiang Luo; Yiqian Xia; Yuanchi Yan; Haidong Wang;\n22 Long Short-Term Session Search: Joint Personalized Reranking and Next Query Prediction\nHighlight: We propose a personalized session search model, called Long short-term session search, Network (LostNet), that jointly learns to rerank documents for the current query and predict the next query. Qiannan Cheng; Zhaochun Ren; Yujie Lin; Pengjie Ren; Zhumin Chen; Xiangyuan Liu; Maarten de de Rijke;\n23 On The Value of Wikipedia As A Gateway to The Web\nHighlight: To date, however, little is known about the amount of traffic generated by Wikipedia’s external links. We fill this gap in a detailed analysis of usage logs gathered from Wikipedia users’ client devices. Tiziano Piccardi; Miriam Redi; Giovanni Colavizza; Robert West;\n24 Projected Hamming Dissimilarity for Bit-Level Importance Coding in Collaborative Filtering\nHighlight: We propose a variational hashing model for learning hash codes optimized for this projected Hamming dissimilarity, and experimentally evaluate it in collaborative filtering experiments. Christian Hansen; Casper Hansen; Jakob Grue Simonsen; Christina Lioma;\n25 Constructing A Comparison-based Click Model for Web Search\nHighlight: This finding leads to the design of a novel click model named Comparison-based Click Model (CBCM). Ruizhe Zhang; Xiaohui Xie; Jiaxin Mao; Yiqun Liu; Min Zhang; Shaoping Ma;\n26 WiseTrans: Adaptive Transport Protocol Selection for Mobile Web Service\nHighlight: In this paper, we present WiseTrans to adaptively switch transport protocols for mobile web service online and improve the completion time of web requests. Jia Zhang; Enhuan Dong; Zili Meng; Yuan Yang; Mingwei Xu; Sijie Yang; Miao Zhang; Yang Yue;\n27 Surrounded By The Clouds: A Comprehensive Cloud Reachability Study\nHighlight: In this paper, we provide a comprehensive cloud reachability study as we perform extensive global client-to-cloud latency measurements towards 189 datacenters from all major cloud providers. Lorenzo Corneo; Maximilian Eder; Nitinder Mohan; Aleksandr Zavodovski; Suzan Bayhan; Walter Wong; Per Gunningberg; Jussi Kangasharju; Jörg Ott;\n28 BrowseLite: A Private Data Saving Solution for The Web\nHighlight: In this paper, we argue that data-saving is still possible without impacting either users privacy or Web-compat. Conor Kelton; Matteo Varvello; Andrius Aucinas; Ben Livshits;\n29 Superways: A Datacenter Topology for Incast-heavy Workloads\nHighlight: We propose Superways, a heterogeneous datacenter topology that provides higher bandwidth for some servers to absorb incasts, as incasts occur only at a small number of servers that aggregate responses from other senders. Hamed Rezaei; Balajee Vamanan;\n30 BBR Bufferbloat in DASH Video\nHighlight: Our investigation reveals that BBR under deep buffers and high network burstiness severely overestimates available bandwidth and does not converge to steady state, both of which results in BBR sending substantially more data into the network, causing a queue buildup. Santiago Vargas; Rebecca Drucker; Aiswarya Renganathan; Aruna Balasubramanian; Anshul Gandhi;\n31 Graph Structure Estimation Neural Networks\nHighlight: In this paper, we propose graph estimation neural networks GEN, which estimates graph structure for GNNs. Ruijia Wang; Shuai Mou; Xiao Wang; Wanpeng Xiao; Qi Ju; Chuan Shi; Xing Xie;\n32 Efficient Probabilistic Truss Indexing on Uncertain Graphs\nHighlight: In this paper, we study the problem of (k, ?)-truss indexing and querying over an uncertain graph . Zitan Sun; Xin Huang; Jianliang Xu; Francesco Bonchi;\n33 Random Graphs with Prescribed K-Core Sequences: A New Null Model for Network Analysis\nHighlight: Guided by this challenge, we establish a new family of network null models that operate on the k-core decomposition. Katherine Van Koevering; Austin Benson; Jon Kleinberg;\n34 Motif-driven Dense Subgraph Discovery in Directed and Labeled Networks\nHighlight: In this work, we propose quark decomposition framework to locate dense subgraphs that are rich with a given motif. Ahmet Erdem Sarıyüce;\n35 Heterogeneous Graph Neural Network Via Attribute Completion\n36 DGCN: Diversified Recommendation with Graph Convolutional Networks\nHighlight: In this paper, we aim at pushing the diversification to the upstream candidate generation stage, with the help of Graph Convolutional Networks (GCN). Yu Zheng; Chen Gao; Liang Chen; Depeng Jin; Yong Li;\n37 Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation\nHighlight: In this paper, we fill this gap and propose a multi-channel hypergraph convolutional network to enhance social recommendation by leveraging high-order user relations. Junliang Yu; Hongzhi Yin; Jundong Li; Qinyong Wang; Nguyen Quoc Viet Hung; Xiangliang Zhang;\n38 Reinforcement Recommendation with User Multi-aspect Preference\nHighlight: In this paper, we consider how to model user multi-aspect preferences in the context of RL-based recommender system. Xu Chen; Yali Du; Long Xia; Jun Wang;\n39 Variation Control and Evaluation for Generative Slate Recommendations\nHighlight: In this paper, we propose to enhance the accuracy-based evaluation with slate variation metrics to estimate the stochastic behavior of generative models. Shuchang Liu; Fei Sun; Yingqiang Ge; Changhua Pei; Yongfeng Zhang;\n40 Adversarial and Contrastive Variational Autoencoder for Sequential Recommendation\nHighlight: To solve the above problem, in this work, we propose a novel method called Adversarial and Contrastive Variational Autoencoder (ACVAE) for sequential recommendation. Zhe Xie; Chengxuan Liu; Yichi Zhang; Hongtao Lu; Dong Wang; Yue Ding;\n41 “Is It A Qoincidence?”: An Exploratory Study of QAnon on Voat\nHighlight: This paper provides an empirical exploratory analysis of the QAnon community on Voat.co, a Reddit-esque news aggregator, which has captured the interest of the press for its toxicity and for providing a platform to QAnon followers. Antonis Papasavva; Jeremy Blackburn; Gianluca Stringhini; Savvas Zannettou; Emiliano De Cristofaro;\n42 Chinese Wall or Swiss Cheese? Keyword filtering in the Great Firewall of China\nHighlight: We report on a detailed investigation of the GFW’s application-layer understanding of HTTP. Zachary Weinberg; Diogo Barradas; Nicolas Christin;\n43 Understanding The Impact of Encrypted DNS on Internet Censorship\nHighlight: In this paper, we study the impact of the encrypted DNS on Internet censorship in two aspects. Lin Jin; Shuai Hao; Haining Wang; Chase Cotton;\n44 Improving Cyberbullying Detection with User Interaction\nHighlight: In this work, we observe that user comments in the same session may be inherently related, e.g., discussing similar topics, and their interaction may evolve over time. Suyu Ge; Lu Cheng; Huan Liu;\n45 IFSpard: An Information Fusion-based Framework for Spam Review Detection\nHighlight: To solve these problems, we propose IFSpard, a novel information fusion-based framework that aims at exploring and exploiting useful information from various aspects for spam review detection. Yao Zhu; Hongzhi Liu; Yingpeng Du; Zhonghai Wu;\n46 Dr.Emotion: Disentangled Representation Learning for Emotion Analysis on Social Media to Improve Community Resilience in The COVID-19 Era and Beyond\nHighlight: To help combat the prolonged pandemic that has exposed vulnerabilities impacting community resilience, in this paper, based on our established large-scale COVID-19 related social media data, we propose and develop an integrated framework (named Dr.Emotion) to learn disentangled representations of social media posts (i.e., tweets) for emotion analysis and thus to gain deep insights into public perceptions towards COVID-19. Mingxuan Ju; Wei Song; Shiyu Sun; Yanfang Ye; Yujie Fan; Shifu Hou; Kenneth Loparo; Liang Zhao;\n47 Modeling Human Motives and Emotions from Personal Narratives Using External Knowledge And Entity Tracking\nHighlight: In this work, we propose a Transformer-based architecture, referred to as , to model characters’ motives and emotions from personal narratives. Prashanth Vijayaraghavan; Deb Roy;\n48 Curriculum CycleGAN for Textual Sentiment Domain Adaptation with Multiple Sources\nHighlight: In this paper, we propose a novel instance-level MDA framework, named curriculum cycle-consistent generative adversarial network (C-CycleGAN), to address the above issues. Sicheng Zhao; Yang Xiao; Jiang Guo; Xiangyu Yue; Jufeng Yang; Ravi Krishna; Pengfei Xu; Kurt Keutzer;\n49 Latent Target-Opinion As Prior for Document-Level Sentiment Classification: A Variational Approach from Fine-Grained Perspective\nHighlight: In this paper, we draw inspiration from fine-grained sentiment analysis, proposing to first learn the latent target-opinion distribution behind the documents, and then leverage such fine-grained prior knowledge into the classification process. Hao Fei; Yafeng Ren; Shengqiong Wu; Bobo Li; Donghong Ji;\n50 Contrastive Lexical Diffusion Coefficient: Quantifying The Stickiness of The Ordinary\nHighlight: In this study we introduce a new metric, contrastive lexical diffusion (CLD) coefficient, which attempts to measure the degree to which ordinary language (here clusters of common words) catch on over friendship connections over time. Mohammadzaman Zamani; H. Andrew Schwartz;\n51 High-dimensional Sparse Embeddings for Collaborative Filtering\nHighlight: We propose a new recommendation model based on a full-rank factorization of the inverse Gram matrix. Jan Van Balen; Bart Goethals;\n52 Sinkhorn Collaborative Filtering\nHighlight: In this paper, we develop a unified view for the existing latent factor models from a probabilistic perspective. Xiucheng Li; Jin Yao Chin; Yile Chen; Gao Cong;\n53 HGCF: Hyperbolic Graph Convolution Networks for Collaborative Filtering\nHighlight: In this paper we combine these frameworks in a novel way, by proposing a hyperbolic GCN model for collaborative filtering. Jianing Sun; Zhaoyue Cheng; Saba Zuberi; Felipe Perez; Maksims Volkovs;\n54 Collaborative Filtering with Preferences Inferred from Brain Signals\nHighlight: Here, we use brain-computer interfacing to infer preferences directly from the human brain. Keith M. Davis III; Michiel Spapé; Tuukka Ruotsalo;\n55 Variable Interval Time Sequence Modeling for Career Trajectory Prediction: Deep Collaborative Perspective\nHighlight: To this end, in this paper, we propose a unified time-aware career trajectory prediction framework, namely TACTP, which is capable of jointly providing the above three abilities for better understanding the career trajectories of talents. Chao Wang; Hengshu Zhu; Qiming Hao; Keli Xiao; Hui Xiong;\n56 User-oriented Fairness in Recommendation\n57 Mitigating Gender Bias in Captioning Systems\nHighlight: To alleviate the unwanted bias, we propose a new Guided Attention Image Captioning model (GAIC) which provides self-guidance on visual attention to encourage the model to capture correct gender visual evidence. Ruixiang Tang; Mengnan Du; Yuening Li; Zirui Liu; Na Zou; Xia Hu;\n58 Fair Partitioning of Public Resources: Redrawing District Boundary to Minimize Spatial Inequality in School Funding\nHighlight: In this paper, we focus on the spatial geometry/distribution of such inequality, i.e., how the highly funded and lesser funded school districts are located relative to each other. Nuno Mota; Negar Mohammadi; Palash Dey; Krishna P. Gummadi; Abhijnan Chakraborty;\n59 Understanding User Sensemaking in Machine Learning Fairness Assessment Systems\nHighlight: In this paper, we seek to inform the design of these systems by examining how individuals make sense of fairness issues as they use different de-biasing affordances. Ziwei Gu; Jing Nathan Yan; Jeffrey M. Rzeszotarski;\n60 Not All Features Are Equal: Discovering Essential Features for Preserving Prediction Privacy\nHighlight: We formulate this problem as a gradient-based perturbation maximization method that discovers this subset in the input feature space with respect to the functionality of the prediction model used by the provider. Fatemehsadat Mireshghallah; Mohammadkazem Taram; Ali Jalali; Ahmed Taha Taha Elthakeb; Dean Tullsen; Hadi Esmaeilzadeh;\n61 Twin Peaks, A Model for Recurring Cascades\nHighlight: In this paper, we propose the first mathematical model that provably explains this interesting phenomenon, besides exhibiting other fundamental properties of information cascades. Matteo Almanza; Silvio Lattanzi; Alessandro Panconesi; Giuseppe Re;\n62 TEDIC: Neural Modeling of Behavioral Patterns in Dynamic Social Interaction Networks\nHighlight: Here we fill this gap by proposing a novel framework, termed TEmporal network-DIffusion Convolutional networks (TEDIC), for generic representation learning on dynamic social interaction networks. Yanbang Wang; Pan Li; Chongyang Bai; Jure Leskovec;\n63 Modeling Sparse Information Diffusion at Scale Via Lazy Multivariate Hawkes Processes\nHighlight: In this work, we propose a novel model and computational approach to overcome this important limitation. Maximilian Nickel; Matthew Le;\n64 DYMOND: DYnamic MOtif-NoDes Network Generative Model\nHighlight: To address these issues, in this work we propose DYnamic MOtif-NoDes (DYMOND)—a generative model that considers (i) the dynamic changes in overall graph structure using temporal motif activity and (ii) the roles nodes play in motifs (e.g., one node plays the hub role in a wedge, while the remaining two act as spokes). Giselle Zeno; Timothy La Fond; Jennifer Neville;\n65 Radflow: A Recurrent, Aggregated, and Decomposable Model for Networks of Time Series\nHighlight: We propose a new model for networks of time series that influence each other. Alasdair Tran; Alexander Mathews; Cheng Soon Ong; Lexing Xie;\n66 Towards A Better Understanding of Query Reformulation Behavior in Web Search\nHighlight: Different from existing efforts that rely on external assessors to make judgments, in the field study we collect both implicit behavior signals and explicit user feedback information. Jia Chen; Jiaxin Mao; Yiqun Liu; Fan Zhang; Min Zhang; Shaoping Ma;\n67 Topic-enhanced Knowledge-aware Retrieval Model for Diverse Relevance Estimation\nHighlight: In this paper, we propose a Topic Enhanced Knowledge-aware retrieval Model (TEKM) that jointly learns semantic similarity, knowledge relevance and topical relatedness to estimate relevance between query and document. Xiangsheng Li; Jiaxin Mao; Weizhi Ma; Yiqun Liu; Min Zhang; Shaoping Ma; Zhaowei Wang; Xiuqiang He;\n68 Controllable Gradient Item Retrieval\nHighlight: In this paper, we identify and study an important problem of gradient item retrieval. Haonan Wang; Chang Zhou; Carl Yang; Hongxia Yang; Jingrui He;\n69 Graph-based Hierarchical Relevance Matching Signals for Ad-hoc Retrieval\nHighlight: Therefore, we propose a Graph-based Hierarchical Relevance Matching model (GHRM) for ad-hoc retrieval, by which we can capture the subtle and general hierarchical matching signals simultaneously. Xueli Yu; Weizhi Xu; Zeyu Cui; Shu Wu; Liang Wang;\n70 Cross-Positional Attention for Debiasing Clicks\nHighlight: In this work, we propose a fully data-driven neural model for the examination bias, Cross-Positional Attention (XPA), which is more flexible in fitting complex user behaviors. Honglei Zhuang; Zhen Qin; Xuanhui Wang; Michael Bendersky; Xinyu Qian; Po Hu; Dan Chary Chen;\n71 Inductive Entity Representations from Text Via Link Prediction\nHighlight: Therefore, in this work, we propose a holistic evaluation protocol for entity representations learned via a link prediction objective. Daniel Daza; Michael Cochez; Paul Groth;\n72 Revisiting The Evaluation Protocol of Knowledge Graph Completion Methods for Link Prediction\nHighlight: In this paper, we contribute to the evaluation of link prediction as follows: 1) We propose a variation of the mean rank that considers the number of negative counterparts. Sudhanshu Tiwari; Iti Bansal; Carlos R. Rivero;\n73 Boosting The Speed of Entity Alignment 10 ×: Dual Attention Matching Network with Normalized Hard Sample Mining\nHighlight: In this paper, we propose a novel KG encoder — Dual Attention Matching Network (Dual-AMN), which not only models both intra-graph and cross-graph information smartly, but also greatly reduces computational complexity. Xin Mao; Wenting Wang; Yuanbin Wu; Man Lan;\n74 Progressive, Holistic Geospatial Interlinking\nHighlight: In this work, we introduce a batch algorithm that simultaneously computes all topological relations and define the task of Progressive Geospatial Interlinking, which produces results in a pay-as-you-go manner when the available computational or temporal resources are limited. George Papadakis; Georgios Mandilaras; Nikos Mamoulis; Manolis Koubarakis;\n75 RETA: A Schema-Aware, End-to-End Solution for Instance Completion in Knowledge Graphs\nHighlight: We propose an end-to-end solution called RETA (as it suggests the Relation and Tail for a given head entity) consisting of two components: a RETA-Filter and RETA-Grader. Paolo Rosso; Dingqi Yang; Natalia Ostapuk; Philippe Cudré-Mauroux;\n76 A Recommender System for Crowdsourcing Food Rescue Platforms\nHighlight: In this paper, we propose the first machine learning based model to improve volunteer engagement in the food waste and security domain. Zheyuan Ryan Shi; Leah Lizarondo; Fei Fang;\n77 A Workflow Analysis of Context-driven Conversational Recommendation\nHighlight: In this paper, we aim to understand a general workflow of natural context-driven conversational recommendation that arises from a pairwise study of a human user interacting with a human simulating the role of a recommender. Shengnan Lyu; Arpit Rana; Scott Sanner; Mohamed Reda Bouadjenek;\n78 Learning Intents Behind Interactions with Knowledge Graph for Recommendation\nHighlight: In this study, we explore intents behind a user-item interaction by using auxiliary item knowledge, and propose a new model, Knowledge Graph-based Intent Network (KGIN). Xiang Wang; Tinglin Huang; Dingxian Wang; Yancheng Yuan; Zhenguang Liu; Xiangnan He; Tat-Seng Chua;\n79 Rabbit Holes and Taste Distortion: Distribution-Aware Recommendation with Evolving Interests\nHighlight: We empirically identify this taste distortion problem through a data-driven study over multiple datasets. Xing Zhao; Ziwei Zhu; James Caverlee;\n80 DeepRec: On-device Deep Learning for Privacy-Preserving Sequential Recommendation in Mobile Commerce\nHighlight: To address the issue, this paper proposes DeepRec, an on-device deep learning framework of mining interaction behaviors for sequential recommendation without sending any raw data or intermediate results out of the device, preserving user privacy maximally. Jialiang Han; Yun Ma; Qiaozhu Mei; Xuanzhe Liu;\n81 Meta-HAR: Federated Representation Learning for Human Activity Recognition\nHighlight: In this paper, we propose Meta-HAR, a federated representation learning framework, in which a signal embedding network is meta-learned in a federated manner, while the learned signal representations are further fed into a personalized classification network at each user for activity prediction. Chenglin Li; Di Niu; Bei Jiang; Xiao Zuo; Jianming Yang;\n82 PFA: Privacy-preserving Federated Adaptation for Effective Model Personalization\nHighlight: In this paper, we attempt to realize personalization beyond a single client. Bingyan Liu; Yao Guo; Xiangqun Chen;\n83 Characterizing Impacts of Heterogeneity in Federated Learning Upon Large-Scale Smartphone Data\nHighlight: In this paper, we carry out the first empirical study to characterize the impacts of heterogeneity in FL. Chengxu Yang; Qipeng Wang; Mengwei Xu; Zhenpeng Chen; Kaigui Bian; Yunxin Liu; Xuanzhe Liu;\n84 Incentive Mechanism for Horizontal Federated Learning Based on Reputation and Reverse Auction\nHighlight: Therefore, this paper proposes a federated learning incentive mechanism based on reputation and reverse auction theory. Jingwen Zhang; Yuezhou Wu; Rong Pan;\n85 Hierarchical Personalized Federated Learning for User Modeling\nHighlight: In this paper, we propose a novel client-server architecture framework, namely Hierarchical Personalized Federated Learning (HPFL) to serve federated learning in user modeling with inconsistent clients. Jinze Wu; Qi Liu; Zhenya Huang; Yuting Ning; Hao Wang; Enhong Chen; Jinfeng Yi; Bowen Zhou;\n86 Data Poisoning Attacks and Defenses to Crowdsourcing Systems\nHighlight: Specifically, we show that crowdsourcing is vulnerable to data poisoning attacks, in which malicious clients provide carefully crafted data to corrupt the aggregated data. Minghong Fang; Minghao Sun; Qi Li; Neil Zhenqiang Gong; Jin Tian; Jia Liu;\n87 Deepfake Videos in The Wild: Analysis and Detection\nHighlight: To bridge this gap in knowledge, we make the following contributions: First, we collect and present the largest dataset of deepfake videos in the wild, containing 1,869 videos from YouTube and Bilibili, and extract over 4.8M frames of content. Second, we present a comprehensive analysis of the growth patterns, popularity, creators, manipulation strategies, and production methods of deepfake content in the real-world. Jiameng Pu; Neal Mangaokar; Lauren Kelly; Parantapa Bhattacharya; Kavya Sundaram; Mobin Javed; Bolun Wang; Bimal Viswanath;\n88 RIGA: Covert and Robust White-Box Watermarking of Deep Neural Networks\nHighlight: In this paper, we generalize white-box watermarking algorithms for DNNs, where the data owner needs white-box access to the model to extract the watermark. Tianhao Wang; Florian Kerschbaum;\n89 CoResident Evil: Covert Communication In The Cloud With Lambdas\nHighlight: In this work, we explore the feasibility of constructing a practical covert channel from lambdas. Anil Yelam; Shibani Subbareddy; Keerthana Ganesan; Stefan Savage; Ariana Mirian;\n90 DAPter: Preventing User Data Abuse in Deep Learning Inference Services\nHighlight: In this work, we propose, to our best knowledge, the first data abuse prevention mechanism called DAPter. Hao Wu; Xuejin Tian; Yuhang Gong; Xing Su; Minghao Li; Fengyuan Xu;\n91 Cross-lingual Language Model Pretraining for Retrieval\nHighlight: In this paper, we introduce two novel retrieval-oriented pretraining tasks to further pretrain cross-lingual language models for downstream retrieval tasks such as cross-lingual ad-hoc retrieval (CLIR) and cross-lingual question answering (CLQA). Puxuan Yu; Hongliang Fei; Ping Li;\n92 Match Plan Generation in Web Search with Parameterized Action Reinforcement Learning\nHighlight: In this work, we formulate the match plan generation as a Partially Observable Markov Decision Process (POMDP) with a parameterized action space, and propose a novel reinforcement learning algorithm Parameterized Action Soft Actor-Critic (PASAC) to effectively enhance the exploration in both spaces. Ziyan Luo; Linfeng Zhao; Wei Cheng; Sihao Chen; Qi Chen; Hui Xue; Haidong Wang; Chuanjie Liu; Mao Yang; Lintao Zhang;\n93 A Linguistic Study on Relevance Modeling in Information Retrieval\nHighlight: We proposed three intervention methods to investigate how to leverage different modeling focuses of relevance to improve these IR tasks. Yixing Fan; Jiafeng Guo; Xinyu Ma; Ruqing Zhang; Yanyan Lan; Xueqi Cheng;\n94 Estimation of Fair Ranking Metrics with Incomplete Judgments\nHighlight: In order to address this problem, we propose a sampling strategy and estimation technique for four fair ranking metrics. Ömer Kırnap; Fernando Diaz; Asia Biega; Michael Ekstrand; Ben Carterette; Emine Yilmaz;\n95 Pivot-based Candidate Retrieval for Cross-lingual Entity Linking\nHighlight: In this paper, we propose a pivot-based approach which inherits the advantages of the aforementioned two approaches while avoiding their limitations. Qian Liu; Xiubo Geng; Jie Lu; Daxin Jiang;\n96 The Structure of Toxic Conversations on Twitter\nHighlight: In this paper, we study the relationship between structure and toxicity in conversations on Twitter. Martin Saveski; Brandon Roy; Deb Roy;\n97 Interventions for Softening Can Lead to Hardening of Opinions: Evidence from a Randomized Controlled Trial\nHighlight: Motivated by the goal of designing interventions for softening polarized opinions on the Web, and building on results from psychology, we hypothesized that people would be moved more easily towards opposing opinions when the latter were voiced by a celebrity they like, rather than by a celebrity they dislike. Andreas Spitz; Ahmad Abu-Akel; Robert West;\n98 “Short Is The Road That Leads from Fear to Hate”: Fear Speech in Indian WhatsApp Groups\nHighlight: In this paper, we perform the first large scale study on fear speech across thousands of public WhatsApp groups discussing politics in India. Punyajoy Saha; Binny Mathew; Kiran Garimella; Animesh Mukherjee;\n99 “Go Eat A Bat, Chang!”: On The Emergence of Sinophobic Behavior on Web Communities in The Face of COVID-19\nHighlight: In this paper, we make a first attempt to study the emergence of Sinophobic behavior on the Web during the outbreak of the COVID-19 pandemic. Fatemeh Tahmasbi; Leonard Schild; Chen Ling; Jeremy Blackburn; Gianluca Stringhini; Yang Zhang; Savvas Zannettou;\n100 Conversations Gone Alright: Quantifying and Predicting Prosocial Outcomes in Online Conversations\nHighlight: We introduce a series of new theory-inspired metrics to define prosocial outcomes such as mentoring and esteem enhancement. Jiajun Bao; Junjie Wu; Yiming Zhang; Eshwar Chandrasekharan; David Jurgens;\n101 DF-TAR: A Deep Fusion Network for Citywide Traffic Accident Risk Prediction with Dangerous Driving Behavior\nHighlight: In this paper, we propose a Deep Fusion network for citywide Traffic Accident Risk prediction (DF-TAR) with dangerous driving statistics that contain the frequencies of various dangerous driving offences in each region. Patara Trirat; Jae-Gil Lee;\n102 Dissecting Performance of Production QUIC\nHighlight: In this paper, we conduct one of the first comparative studies on the performance of QUIC and TCP against production endpoints hosted by Google, Facebook, and Cloudflare under various dimensions: network conditions, workloads, and client implementations. Alexander Yu; Theophilus A. Benson;\n103 XY-Sketch: on Sketching Data Streams at Web Scale\nHighlight: In this paper, we propose a novel structure, called XY-sketch, which estimates the frequency of a data item by estimating the probability of this item appearing in the data stream. Yongqiang Liu; Xike Xie;\n104 NTAM: Neighborhood-Temporal Attention Model for Disk Failure Prediction in Cloud Platforms\nHighlight: In this paper, we propose Neighborhood-Temporal Attention Model (NTAM), a novel deep learning based approach to disk failure prediction. Chuan Luo; Pu Zhao; Bo Qiao; Youjiang Wu; Hongyu Zhang; Wei Wu; Weihai Lu; Yingnong Dang; Saravanakumar Rajmohan; Qingwei Lin; Dongmei Zhang;\n105 WebSocket Adoption and The Landscape of The Real-Time Web\nHighlight: This paper investigates this ecosystem and reports on the prevalence, benefits, and drawbacks of these technologies, with a particular focus on the adoption of WebSockets. Paul Murley; Zane Ma; Joshua Mason; Michael Bailey; Amin Kharraz;\n106 Theoretically Improving Graph Neural Networks Via Anonymous Walk Graph Kernels\nHighlight: In this paper, we propose GSKN1, a GNN model with a theoretically stronger ability to distinguish graph structures. Qingqing Long; Yilun Jin; Yi Wu; Guojie Song;\n107 Interpreting and Unifying Graph Neural Networks with An Optimization Framework\n108 Extract The Knowledge of Graph Neural Networks and Go Beyond It: An Effective Knowledge Distillation Framework\nHighlight: In this paper, we propose a framework based on knowledge distillation to address the above issues. Cheng Yang; Jiawei Liu; Chuan Shi;\n109 CurGraph: Curriculum Learning for Graph Classification\nHighlight: To address this issue, we present the CurGraph (Curriculum Learning for Graph Classification) framework, that analyzes the graph difficulty in the high-level semantic feature space. Yiwei Wang; Wei Wang; Yuxuan Liang; Yujun Cai; Bryan Hooi;\n110 Lorentzian Graph Convolutional Networks\nHighlight: In this paper, we propose a novel hyperbolic GCN named Lorentzian graph convolutional network (LGCN), which rigorously guarantees the learned node features follow the hyperbolic geometry. Yiding Zhang; Xiao Wang; Chuan Shi; Nian Liu; Guojie Song;\n111 Linear-Time Self Attention with Codeword Histogram for Efficient Recommendation\nHighlight: Inspired by the idea of vector quantization that uses cluster centroids to approximate items, we propose LISA (LInear-time Self Attention), which enjoys both the effectiveness of vanilla self-attention and the efficiency of sparse attention. Yongji Wu; Defu Lian; Neil Zhenqiang Gong; Lu Yin; Mingyang Yin; Jingren Zhou; Hongxia Yang;\n112 Learning Heterogeneous Temporal Patterns of User Preference for Timely Recommendation\nHighlight: In this paper, we first define the unique characteristics of the two kinds of temporal pattern of user preference that should be considered in time-aware recommender systems. Then we propose a novel recommender system for timely recommendations, called TimelyRec, which jointly learns the heterogeneous temporal patterns of user preference considering all of the defined characteristics. Junsu Cho; Dongmin Hyun; Seongku Kang; Hwanjo Yu;\n113 Drug Package Recommendation Via Interaction-aware Graph Induction\nHighlight: To that end, in this paper, we target at developing a new paradigm for drug package recommendation with considering the interaction effect within drugs, in which the interaction effects could be affected by patient conditions. Zhi Zheng; Chao Wang; Tong Xu; Dazhong Shen; Penggang Qin; Baoxing Huai; Tongzhu Liu; Enhong Chen;\n114 Interest-aware Message-Passing GCN for Recommendation\nHighlight: In this paper, we propose a novel Interest-aware Message-Passing GCN (IMP-GCN) recommendation model, which performs high-order graph convolution inside subgraphs. Fan Liu; Zhiyong Cheng; Lei Zhu; Zan Gao; Liqiang Nie;\n115 Task-adaptive Neural Process for User Cold-Start Recommendation\nHighlight: In this paper, we develop a novel meta-learning recommender called task-adaptive neural process (TaNP). Xixun Lin; Jia Wu; Chuan Zhou; Shirui Pan; Yanan Cao; Bin Wang;\n116 Consistent Sampling Through Extremal Process\nHighlight: In this paper, we propose using extremal processes to generate samples for estimating the Jaccard similarity. Ping Li; Xiaoyun Li; Gennady Samorodnitsky; Weijie Zhao;\n117 Beyond Outlier Detection: Outlier Interpretation By Attention-Guided Triplet Deviation Network\nHighlight: Accordingly, this paper proposes a novel Attention-guided Triplet deviation network for Outlier interpretatioN (ATON). Hongzuo Xu; Yijie Wang; Songlei Jian; Zhenyu Huang; Yongjun Wang; Ning Liu; Fei Li;\n118 Fair and Representative Subset Selection from Data Streams\nHighlight: In this work, we consider the setting where data items in the stream belong to one of several disjoint groups and investigate the optimization problem with an additional fairness constraint that limits selection to a given number of items from each group. Yanhao Wang; Francesco Fabbri; Michael Mathioudakis;\n119 CLEAR: Contrastive-Prototype Learning with Drift Estimation for Resource Constrained Stream Mining\nHighlight: In this work, we focus on improving the performance of the stream mining approach under the constrained resources, where both the memory resource of old data and labeled new instances are limited/scarce. Zhuoyi Wang; Yuqiao Chen; Chen Zhao; Yu Lin; Xujiang Zhao; Hemeng Tao; Yigong Wang; Latifur Khan;\n120 Diversity on The Go! Streaming Determinantal Point Processes Under A Maximum Induced Cardinality Objective\nHighlight: In this work, we give the first streaming algorithm for optimizing DPPs under the Maximum Induced Cardinality (MIC) objective of Gillenwater et al. . Paul Liu; Akshay Soni; Eun Yong Kang; Yajun Wang; Mehul Parsana;\n121 Self-Supervised Hyperboloid Representations from Logical Queries Over Knowledge Graphs\nHighlight: We propose Hyperboloid Embeddings (HypE), a novel self-supervised dynamic reasoning framework, that utilizes positive first-order existential queries on a KG to learn representations of its entities and relations as hyperboloids in a Poincaré ball. Nurendra Choudhary; Nikhil Rao; Sumeet Katariya; Karthik Subbian; Chandan K. Reddy;\n122 ColChain: Collaborative Linked Data Networks\nHighlight: In this paper, we therefore propose ColChain (COLlaborative knowledge CHAINs), a novel decentralized architecture based on blockchains that not only lowers the burden for the data providers but at the same time also allows users to propose updates to faulty or outdated data, trace updates back to their origin, and query older versions of the data. Christian Aebeloe; Gabriela Montoya; Katja Hose;\n123 MedPath: Augmenting Health Risk Prediction Via Medical Knowledge Paths\nHighlight: In this paper, we propose MedPath to solve these challenges and augment existing risk prediction models with the ability to use personalized information and provide reliable interpretations inferring from disease progression paths. Muchao Ye; Suhan Cui; Yaqing Wang; Junyu Luo; Cao Xiao; Fenglong Ma;\n124 Efficient Computation of Semantically Cohesive Subgraphs for Keyword-Based Knowledge Graph Exploration\nHighlight: In this paper, we formulate it as a quadratic group Steiner tree problem (QGSTP) by extending the classical minimum-weight GST problem which is NP-hard. Yuxuan Shi; Gong Cheng; Trung-Kien Tran; Evgeny Kharlamov; Yulin Shen;\n125 WiseKG: Balanced Access to Web Knowledge Graphs\nHighlight: In this paper, to the best of our knowledge, we present the first work that combines both client-side and server-side query optimization techniques in a truly dynamic fashion: we introduce WiseKG, a system that employs a cost model that dynamically delegates the load between servers and clients by combining client-side processing of shipped partitions with efficient server-side processing of star-shaped sub-queries, based on current server workload and client capabilities. Amr Azzam; Christian Aebeloe; Gabriela Montoya; Ilkcan Keles; Axel Polleres; Katja Hose;\n126 A Longitudinal Study of Removed Apps in IOS App Store\nHighlight: To fill the void, in this paper, we present a large-scale and longitudinal study of removed apps in iOS app store. Fuqi Lin; Haoyu Wang; Liu Wang; Xuanzhe Liu;\n127 Demystifying Illegal Mobile Gambling Apps\nHighlight: However, to the best of our knowledge, mobile gambling apps have not been investigated by our research community. In this paper, we take the first step to fill the void. Yuhao Gao; Haoyu Wang; Li Li; Xiapu Luo; Guoai Xu; Xuanzhe Liu;\n128 ReACt: A Resource-centric Access Control System for Web-app Interactions on Android\nHighlight: In this paper, we propose ReACt, a novel Resource-centric Access Control design that can coherently work with all the web-app interaction mechanisms while addressing the above-mentioned limitations. Xin Zhang; Yifan Zhang;\n129 NeuroPose: 3D Hand Pose Tracking Using EMG Wearables\nHighlight: This paper presents NeuroPose, a system that shows the feasibility of 3D finger motion tracking using a platform of wearable ElectroMyoGraphy (EMG) sensors. Yilin Liu; Shijia Zhang; Mahanth Gowda;\n130 Whale Watching in Inland Indonesia: Analyzing A Small, Remote, Internet-Based Community Cellular Network\nHighlight: Through tight integration with a local operator’s infrastructure, we gather a unique dataset to characterize and report a year of interaction between finances, utilization, and performance of a novel, remote, data-only Community LTE Network in Bokondini, Indonesia. Matthew Johnson; Jenny Liang; Michelle Lin; Sudheesh Singanamalla; Kurtis Heimerl;\n131 Learning Neural Point Processes with Latent Graphs\nHighlight: Because these two tasks are nested, we propose to optimize the model parameters through bilevel programming, and develop an efficient solution based on truncated gradient back-propagation. Qiang Zhang; Aldo Lipani; Emine Yilmaz;\n132 OCT-GAN: Neural ODE-based Conditional Tabular GANs\nHighlight: In this work, we significantly improve the utility by designing our generator and discriminator based on neural ordinary differential equations (NODEs). Jayoung Kim; Jinsung Jeon; Jaehoon Lee; Jihyeon Hyeong; Noseong Park;\n133 Neural Collaborative Reasoning\n134 Deep Co-Attention Network for Multi-View Subspace Learning\nHighlight: To address these issues, in this paper, we propose a deep co-attention network for multi-view subspace learning, which aims to extract both the common information and the complementary information in an adversarial setting and provide robust interpretations behind the prediction to the end-users via the co-attention mechanism. Lecheng Zheng; Yu Cheng; Hongxia Yang; Nan Cao; Jingrui He;\n135 ATJ-Net: Auto-Table-Join Network for Automatic Learning on Relational Databases\nHighlight: For automatic learning on relational databases, we propose an auto-table-join network (ATJ-Net). Jinze Bai; Jialin Wang; Zhao Li; Donghui Ding; Ji Zhang; Jun Gao;\n136 A Cooperative Memory Network for Personalized Task-oriented Dialogue Systems with Incomplete User Profiles\nHighlight: This is unrealistic because In this paper, we study personalized TDSs without assuming that user profiles are complete. Jiahuan Pei; Pengjie Ren; Maarten de Rijke;\n137 Stimuli-Sensitive Hawkes Processes for Personalized Student Procrastination Modeling\nHighlight: To resolve these problems, we introduce a new personalized stimuli-sensitive Hawkes process model (SSHP), by jointly modeling all student-assignment pairs and utilizing their similarities, to predict students’ next activity times even when there are no historical observations. Mengfan Yao; Siqian Zhao; Shaghayegh Sahebi; Reza Feyzi Behnagh;\n138 Personalized Treatment Selection Using Causal Heterogeneity\nHighlight: In this work, we develop a framework for personalization through (i) estimation of heterogeneous treatment effect at either a cohort or member-level, followed by (ii) selection of optimal treatment variants for cohorts (or members) obtained through (deterministic or stochastic) constrained optimization. Ye Tu; Kinjal Basu; Cyrus DiCiccio; Romil Bansal; Preetam Nandy; Padmini Jaikumar; Shaunak Chatterjee;\n139 Incremental Spatio-Temporal Graph Learning for Online Query-POI Matching\nHighlight: To this end, in this paper, we propose an Incremental Spatio-Temporal Graph Learning (IncreSTGL) framework for intelligent online query-POI matching. Zixuan Yuan; Hao Liu; Junming Liu; Yanchi Liu; Yang Yang; Renjun Hu; Hui Xiong;\n140 Slot Self-Attentive Dialogue State Tracking\nHighlight: In this paper, we propose a slot self-attention mechanism that can learn the slot correlations automatically. Fanghua Ye; Jarana Manotumruksa; Qiang Zhang; Shenghui Li; Emine Yilmaz;\n141 Dynamic Embeddings for Interaction Prediction\nHighlight: Building on the success of these studies, we propose a novel method called DeePRed that addresses some of their limitations. Zekarias Kefato; Sarunas Girdzijauskas; Nasrullah Sheikh; Alberto Montresor;\n142 Knowledge Embedding Based Graph Convolutional Network\nHighlight: This paper addresses these limitations by proposing a novel framework, namely the Knowledge Embedding based Graph Convolutional Network (KE-GCN), which combines the power of GCNs in graph-based belief propagation and the strengths of advanced knowledge embedding (a.k.a. knowledge graph embedding) methods, and goes beyond. Donghan Yu; Yiming Yang; Ruohong Zhang; Yuexin Wu;\n143 Motif-Preserving Dynamic Attributed Network Embedding\nHighlight: In this work, we present Motif-preserving Temporal Shift Network (MTSN), a novel dynamic network embedding framework that simultaneously models the local high-order structures and temporal evolution for dynamic attributed networks. Zhijun Liu; Chao Huang; Yanwei Yu; Junyu Dong;\n144 Highly Liquid Temporal Interaction Graph Embeddings\nHighlight: We propose HILI (Highly Liquid Temporal Interaction Graph Embeddings) to predict highly liquid embeddings on temporal interaction graphs. Huidi Chen; Yun Xiong; Yangyong Zhu; Philip S. Yu;\n145 Multiplex Bipartite Network Embedding Using Dual Hypergraph Convolutional Networks\nHighlight: In this paper, we develop an unsupervised Dual HyperGraph Convolutional Network (DualHGCN) model that scalably transforms the multiplex bipartite network into two sets of homogeneous hypergraphs and uses spectral hypergraph convolutional operators, along with intra- and inter-message passing strategies to promote information exchange within and across domains, to learn effective node embeddings. Hansheng Xue; Luwei Yang; Vaibhav Rajan; Wen Jiang; Yi Wei; Yu Lin;\n146 Semi-Open Information Extraction\nHighlight: In this paper, we define a new task, namely Semi-Open Information Extraction (SOIE), to address this need. Bowen Yu; Zhenyu Zhang; Jiawei Sheng; Tingwen Liu; Yubin Wang; Yucheng Wang; Bin Wang;\n147 RECON: Relation Extraction Using Knowledge Graph Context in A Graph Neural Network\nHighlight: In this paper, we present a novel method named RECON, that automatically identifies relations in a sentence (sentential relation extraction) and aligns to a knowledge graph (KG). Anson Bastos; Abhishek Nadgeri; Kuldeep Singh; Isaiah Onando Mulang; Saeedeh Shekarpour; Johannes Hoffart; Manohar Kaul;\n148 GNEM: A Generic One-to-Set Neural Entity Matching Framework\nHighlight: In this paper, we propose a generic one-to-set neural framework named GNEM for entity matching. Runjin Chen; Yanyan Shen; Dongxiang Zhang;\n149 Effective Named Entity Recognition with Boundary-aware Bidirectional Neural Networks\nHighlight: In this paper, we propose a novel Boundary-aware Bidirectional Neural Networks (Ba-BNN) model to tackle these problems for neural-based NER. Fei Li; Zheng Wang; Siu Cheung Hui; Lejian Liao; Dandan Song; Jing Xu;\n150 A Trigger-Sense Memory Flow Framework for Joint Entity and Relation Extraction\nHighlight: To this end, we present a Trigger-Sense Memory Flow Framework (TriMF) for joint entity and relation extraction. Yongliang Shen; Xinyin Ma; Yechun Tang; Weiming Lu;\n151 MulDE: Multi-teacher Knowledge Distillation for Low-dimensional Knowledge Graph Embeddings\nHighlight: In this paper, instead of training high-dimensional models, we propose MulDE, a novel knowledge distillation framework, which includes multiple low-dimensional hyperbolic KGE models as teachers and two student components, namely Junior and Senior. Kai Wang; Yu Liu; Qian Ma; Quan Z. Sheng;\n152 Efficient Non-Sampling Knowledge Graph Embedding\nHighlight: To avoid such deficiency, we propose a new framework for KG embedding—Efficient Non-Sampling Knowledge Graph Embedding (NS-KGE). Zelong Li; Jianchao Ji; Zuohui Fu; Yingqiang Ge; Shuyuan Xu; Chong Chen; Yongfeng Zhang;\n153 Structure-Augmented Text Representation Learning for Efficient Knowledge Graph Completion\nHighlight: In this paper, we follow the textual encoding paradigm and aim to alleviate its drawbacks by augmenting it with graph embedding techniques – a complementary hybrid of both paradigms. Bo Wang; Tao Shen; Guodong Long; Tianyi Zhou; Ying Wang; Yi Chang;\n154 An Adversarial Transfer Network for Knowledge Representation Learning\nHighlight: We propose an adversarial embedding transfer network ATransN, which transfers knowledge from one or more teacher knowledge graphs to a target one through an aligned entity set without explicit data leakage. Huijuan Wang; Shuangyin Li; Rong Pan;\n155 Mixed-Curvature Multi-Relational Graph Neural Network for Knowledge Graph Completion\nHighlight: To address this issue, we propose Mixed-Curvature Multi-Relational Graph Neural Network (M2GNN), a generic approach that embeds multi-relational KGs in a mixed-curvature space for knowledge graph completion. Shen Wang; Xiaokai Wei; Cicero Nogueira Nogueira dos Santos; Zhiguo Wang; Ramesh Nallapati; Andrew Arnold; Bing Xiang; Philip S. Yu; Isabel F. Cruz;\n156 Elo-MMR: A Rating System for Massive Multiplayer Competitions\nHighlight: In this paper, we present a novel Bayesian rating system for contests with many participants. Aram Ebtekar; Paul Liu;\n157 DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems\nHighlight: In light of the pros/cons of DCN and existing feature interaction learning approaches, we propose an improved framework DCN-V2 to make DCN more practical in large-scale industrial settings. Ruoxi Wang; Rakesh Shivanna; Derek Cheng; Sagar Jain; Dong Lin; Lichan Hong; Ed Chi;\n158 A Scalable, Adaptive and Sound Nonconvex Regularizer for Low-rank Matrix Learning\nHighlight: In this paper, based on the key insight that adaptive shrinkage on singular values improve empirical performance, we propose a new nonconvex low-rank regularizer called ”nuclear norm minus Frobenius norm” regularizer, which is scalable, adaptive and sound. Yaqing Wang; Quanming Yao; James Kwok;\n159 An Adversarial Imitation Click Model for Information Retrieval\nHighlight: In this paper, we propose a novel framework, Adversarial Imitation Click Model (AICM), based on imitation learning. Xinyi Dai; Jianghao Lin; Weinan Zhang; Shuai Li; Weiwen Liu; Ruiming Tang; Xiuqiang He; Jianye Hao; Jun Wang; Yong Yu;\n160 CrowdGP: A Gaussian Process Model for Inferring Relevance from Crowd Annotations\nHighlight: In this paper, we relax the independence assumption to model task correlation in terms of relevance. Dan Li; Zhaochun Ren; Evangelos Kanoulas;\n161 Fine-Grained Urban Flow Prediction\nHighlight: To address these two challenges, we present a Spatio-Temporal Relation Network (STRN) to predict fine-grained urban flows. Yuxuan Liang; Kun Ouyang; Junkai Sun; Yiwei Wang; Junbo Zhang; Yu Zheng; David Rosenblum; Roger Zimmermann;\n162 AutoSTG: Neural Architecture Search for Predictions of Spatio-Temporal Graph✱\nHighlight: To tackle these challenges, we propose a novel framework, entitled AutoSTG, for automated spatio-temporal graph prediction. Zheyi Pan; Songyu Ke; Xiaodu Yang; Yuxuan Liang; Yong Yu; Junbo Zhang; Yu Zheng;\n163 Intelligent Electric Vehicle Charging Recommendation Based on Multi-Agent Reinforcement Learning\nHighlight: In this paper, we propose a framework, named Multi-Agent Spatio-Temporal Reinforcement Learning (Master), for intelligently recommending public accessible charging stations by jointly considering various long-term spatiotemporal factors. Weijia Zhang; Hao Liu; Fan Wang; Tong Xu; Haoran Xin; Dejing Dou; Hui Xiong;\n164 STUaNet: Understanding Uncertainty in Spatiotemporal Collective Human Mobility\nHighlight: To provide high-quality uncertainty quantification for spatiotemporal forecasting, we propose an uncertainty learning mechanism to simultaneously estimate internal data quality and quantify external uncertainty regarding various contextual interactions. Zhengyang Zhou; Yang Wang; Xike Xie; Lei Qiao; Yuantao Li;\n165 DeepFEC: Energy Consumption Prediction Under Real-World Driving Conditions for Smart Cities\nHighlight: This paper presents a novel framework that identifies vehicle/driving environment-dependent factors to predict energy consumption over a road network based on historical consumption data for different vehicle types. Sayda Elmi; Kian-Lee Tan;\n166 Separ: Towards Regulating Future of Work Multi-Platform Crowdworking Environments with Privacy Guarantees\nHighlight: In this paper, we propose an overall vision exploring the regulation, privacy, and architecture dimensions for the future of work multi-platform crowdworking environments. Mohammad Javad Amiri; Joris Duguépéroux; Tristan Allard; Divyakant Agrawal; Amr El Abbadi;\n167 Online Label Aggregation: A Variational Bayesian Approach\nHighlight: In this paper, we propose a novel online label aggregation framework, BiLA , which employs variational Bayesian inference method and designs a novel stochastic optimization scheme for incremental training. Chi Hong; Amirmasoud Ghiassi; Yichi Zhou; Robert Birke; Lydia Y. Chen;\n168 Peer Grading The Peer Reviews: A Dual-Role Approach for Lightening The Scholarly Paper Review Process\nHighlight: In this work, we propose a human-AI approach that estimates the conformity of reviews to the conference standards. Ines Arous; Jie Yang; Mourad Khayati; Philippe Cudre-Mauroux;\n169 Multi-Session Diversity to Improve User Satisfaction in Web Applications\nHighlight: Given the hardness of those problems, we propose to formulate a constrained optimization problem that optimizes inter diversity, subject to the constraint of intra diversity. Mohammadreza Esfandiari; Ria Mae Borromeo; Sepideh Nikookar; Paras Sakharkar; Sihem Amer-Yahia; Senjuti Basu Roy;\n170 What Do You Mean? Interpreting Image Classification with Crowdsourced Concept Extraction and Analysis\nHighlight: In this paper, we introduce a scalable human-in-the-loop approach for global interpretability. Agathe Balayn; Panagiotis Soilis; Christoph Lofi; Jie Yang; Alessandro Bozzon;\n171 FINN: Feedback Interactive Neural Network for Intent Recommendation\nHighlight: Therefore, we propose a feedback interactive neural network (FINN) to estimate user’s potential search intent more accurately, by making full use of the feedback interaction with the following three parts: 1) Both positive feedback (PF) and negative feedback (NF) information are collected simultaneously. Yatao Yang; Biyu Ma; Jun Tan; Hongbo Deng; Haikuan Huang; Zibin Zheng;\n172 Weakly-Supervised Question Answering with Effective Rank and Weighted Loss Over Candidates\nHighlight: In this paper, we present an effective method to learn a question answering model in a weak supervision way. Haozhe Qin; Jiangang Zhu; Beijun Shen;\n173 Controlling The Risk of Conversational Search Via Reinforcement Learning\nHighlight: In this work, we propose a risk-aware conversational search agent model to balance the risk of answering user’s query and asking clarifying questions. Zhenduo Wang; Qingyao Ai;\n174 Joint Spatio-Textual Reasoning for Answering Tourism Questions\nHighlight: Our goal is to answer real-world tourism questions that seek Points-of-Interest (POI) recommendations. Danish Contractor; Shashank Goel; Mausam; Parag Singla;\n175 Adapting to Context-Aware Knowledge in Natural Conversation for Multi-Turn Response Selection\nHighlight: To this end, we define Adaptive Knowledge-Grounded Conversations (AKGCs) where the knowledge is to ground the conversation within a multi-turn context by adapting to three modes. Chen Zhang; Hao Wang; Feijun Jiang; Hongzhi Yin;\n176 Understanding The Complexity of Detecting Political Ads\nHighlight: We take an empirical approach to analyze what kind of ads are deemed political by ordinary people and what kind of ads lead to disagreement. Vera Sosnovik; Oana Goga;\n177 War of Words II: Enriched Models of Law-Making Processes\nHighlight: We mine a rich dataset of law edits and introduce models predicting their adoption by parliamentary committees. Victor Kristof; Aswin Suresh; Matthias Grossglauser; Patrick Thiran;\n178 Assessing The Effects of Friend-to-Friend Texting OnTurnout in The 2018 US Midterm Elections\nHighlight: We address these challenges, which are likely to impinge upon any study that seeks to randomize authentic friend-to-friend interactions, by tailoring the statistical analysis to make use of additional data about both users and subjects. Aaron Schein; Keyon Vafa; Dhanya Sridhar; Victor Veitch; Jeffrey Quinn; James Moffet; David M. Blei; Donald P. Green;\n179 Fast Evaluation for Relevant Quantities of Opinion Dynamics\nHighlight: In this paper, by reducing the problem of computing relevant quantities to evaluating ?2 norms of some vectors, we present a nearly linear time algorithm to estimate all these quantities. Wanyue Xu; Qi Bao; Zhongzhi Zhang;\n180 Random Walks with Erasure: Diversifying Personalized Recommendations on Social and Information Networks\nHighlight: In this context, we develop a novel recommendation framework with a goal of improving information diversity using a modified random walk exploration of the user-item graph. Bibek Paudel; Abraham Bernstein;\n181 Soft-mask: Adaptive Substructure Extractions for Graph Neural Networks\nHighlight: In this work, we propose to learn graph representations from a sequence of subgraphs of the original graph to better capture task-relevant substructures or hierarchical structures and skip noisy parts. Mingqi Yang; Yanming Shen; Heng Qi; Baocai Yin;\n182 Graph Contrastive Learning with Adaptive Augmentation\nHighlight: In this paper, we propose a novel graph contrastive representation learning method with adaptive augmentation that incorporates various priors for topological and semantic aspects of the graph. Yanqiao Zhu; Yichen Xu; Feng Yu; Qiang Liu; Shu Wu; Liang Wang;\n183 SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information Mechanism\nHighlight: This paper presents a novel hierarchical subgraph-level selection and embedding-based graph neural network for graph classification, namely SUGAR, to learn more discriminative subgraph representations and respond in an explanatory way. Qingyun Sun; Jianxin Li; Hao Peng; Jia Wu; Yuanxing Ning; Philip S. Yu; Lifang He;\n184 Strongly Local Hypergraph Diffusions for Clustering and Semi-supervised Learning\nHighlight: To tackle these issues, this paper proposes a new diffusion-based hypergraph clustering algorithm that solves a quadratic hypergraph cut based objective akin to a hypergraph analog of Andersen-Chung-Lang personalized PageRank clustering for graphs. Meng Liu; Nate Veldt; Haoyu Song; Pan Li; David F. Gleich;\n185 TG-GAN: Continuous-time Temporal Graph Deep Generative Models with Time-Validity Constraints\nHighlight: Here, we propose the “Temporal Graph Generative Adversarial Network” (TG-GAN) for continuous-time graph generation with time-validity constraints 1. Liming Zhang; Liang Zhao; Shan Qin; Dieter Pfoser; Chen Ling;\n186 Cookie Swap Party: Abusing First-Party Cookies for Web Tracking\n187 User Tracking in The Post-cookie Era: How Websites Bypass GDPR Consent to Track Users\nHighlight: In this paper, we explore whether websites use more persistent and sophisticated forms of tracking in order to track users who said they do not want cookies. Emmanouil Papadogiannakis; Panagiotis Papadopoulos; Nicolas Kourtellis; Evangelos P. Markatos;\n188 It’s Not Just The Site, It’s The Contents: Intra-domain Fingerprinting Social Media Websites Through CDN Bursts\nHighlight: In this work, we investigate the intra-domain WPF among social media websites, against the realistic on-path passive attack scenario. Kailong Wang; Junzhe Zhang; Guangdong Bai; Ryan Ko; Jin Song Dong;\nHighlight: In this work, we target to solve the problem of compliance analysis between GDPR (Article 13) and privacy policies. Shuang Liu; Baiyang Zhao; Renjie Guo; Guozhu Meng; Fan Zhang; Meishan Zhang;\n190 Privacy Policies Over Time: Curation and Analysis of A Million-Document Dataset\nRyan Amos; Gunes Acar; Elena Lucherini; Mihir Kshirsagar; Arvind Narayanan; Jonathan Mayer; Ryan Amos; Gunes Acar; Elena Lucherini; Mihir Kshirsagar; Arvind Narayanan; Jonathan Mayer;\n191 STAN: Spatio-Temporal Attention Network for Next Location Recommendation\nHighlight: To aggregate all relevant visits from user trajectory and recall the most plausible candidates from weighted representations, here we propose a Spatio-Temporal Attention Network (STAN) for location recommendation. Yingtao Luo; Qiang Liu; Zhaocheng Liu;\n192 Session-aware Linear Item-Item Models for Session-based Recommendation\nHighlight: In this paper, we propose simple-yet-effective linear models for considering the holistic aspects of the sessions. Minjin Choi; Jinhong Kim; Joonseok Lee; Hyunjung Shim; Jongwuk Lee;\n193 Learning Fair Representations for Recommendation: A Graph-based Perspective\nHighlight: In this paper, we propose a novel graph based technique for ensuring fairness of any recommendation models. Le Wu; Lei Chen; Pengyang Shao; Richang Hong; Xiting Wang; Meng Wang;\n194 Leveraging Review Properties for Effective Recommendation\nHighlight: In this paper, we propose to model the reviews with their associated available properties. Xi Wang; Iadh Ounis; Craig Macdonald;\n195 A Model of Two Tales: Dual Transfer Learning Framework for Improved Long-tail Item Recommendation\nHighlight: Specifically, we propose a novel dual transfer learning framework that jointly learns the knowledge transfer from both model-level and item-level: 1. Yin Zhang; Derek Zhiyuan Cheng; Tiansheng Yao; Xinyang Yi; Lichan Hong; Ed H. Chi;\n196 DeepVista: 16K Panoramic Cinema on Your Mobile Device\nHighlight: In this paper, we design, implement, and evaluate , which is to our knowledge the first consumer-class system that streams panoramic videos far beyond the ultra high-definition resolution (up to 16K) to mobile devices, offering truly immersive experiences. Wenxiao Zhang; Feng Qian; Bo Han; Pan Hui;\n197 CoopEdge: A Decentralized Blockchain-based Platform for Cooperative Edge Computing\nHighlight: To tackle these challenges systematically, this paper proposes CoopEdge, a novel blockchain-based decentralized platform, to drive and support cooperative edge computing. Liang Yuan; Qiang He; Siyu Tan; Bo Li; Jiangshan Yu; Feifei Chen; Hai Jin; Yun Yang;\n198 Temporal Analysis of The Entire Ethereum Blockchain Network\nHighlight: In this paper, we investigate the evolutionary nature of Ethereum interaction networks from a temporal graphs perspective. Lin Zhao; Sourav Sen Gupta; Arijit Khan; Robby Luo;\n199 SrVARM: State Regularized Vector Autoregressive Model for Joint Learning of Hidden State Transitions and State-Dependent Inter-Variable Dependencies from Multi-variate Time Series\nHighlight: To solve this problem, we introduce the State-Regularized Vector Autoregressive Model (SrVARM) which combines a state-regularized recurrent neural network to learn the dynamics of transitions between discrete hidden states with an augmented autoregressive model which models the inter-variable dependencies in each state using a state-dependent directed acyclic graph (DAG). Tsung-Yu Hsieh; Yiwei Sun; Xianfeng Tang; Suhang Wang; Vasant G. Honavar;\n200 Equilibrium Inverse Reinforcement Learning for Ride-hailing Vehicle Network\nHighlight: In this work, we formulate the problem of passenger-vehicle matching in a sparsely connected graph and proposed an algorithm to derive an equilibrium policy in a multi-agent environment. Takuma Oda;\n201 Unifying Offline Causal Inference and Online Bandit Learning for Data Driven Decision\nHighlight: To overcome the aforementioned limitations, we propose a framework to unify offline causal inference algorithms (e.g., weighting, matching) and online learning algorithms (e.g., UCB, LinUCB). Ye Li; Hong Xie; Yishi Lin; John C.S. Lui;\n202 Automated Creative Optimization for E-Commerce Advertising\nHighlight: Specifically, motivated by AutoML, we propose one-shot search algorithms for searching effective interaction functions between elements. Jin Chen; Ju Xu; Gangwei Jiang; Tiezheng Ge; Zhiqiang Zhang; Defu Lian; Kai Zheng;\n203 GuideBoot: Guided Bootstrap for Deep Contextual Banditsin Online Advertising\nHighlight: In this paper, we introduce Guided Bootstrap (GuideBoot), combining the best of both worlds. Feiyang Pan; Haoming Li; Xiang Ao; Wei Wang; Yanrong Kang; Ao Tan; Qing He;\n204 A Hybrid Bandit Model with Visual Priors for Creative Ranking in Display Advertising\nHighlight: Our contributions are three-fold: 1) We present a visual-aware ranking model (called VAM) that incorporates a list-wise ranking loss for ordering the creatives according to the visual appearance. Shiyao Wang; Qi Liu; Tiezheng Ge; Defu Lian; Zhiqiang Zhang;\n205 Local Clustering in Contextual Multi-Armed Bandits\nHighlight: In particular, we define the problem of cluster detection in contextual MAB, and propose a bandit algorithm, LOCB, embedded with local clustering procedure. Yikun Ban; Jingrui He;\n206 Learning from Graph Propagation Via Ordinal Distillation for One-Shot Automated Essay Scoring\nHighlight: In this paper, we propose a Transductive Graph-based Ordinal Distillation (TGOD) framework to tackle the task of one-shot AES. Zhiwei Jiang; Meng Liu; Yafeng Yin; Hua Yu; Zifeng Cheng; Qing Gu;\n207 Wiki2Prop: A Multimodal Approach for Predicting Wikidata Properties from Wikipedia\nHighlight: In this work, we focus on entities with a dedicated Wikipedia page in any language to make predictions directly based on textual content. Michael Luggen; Julien Audiffren; Djellel Difallah; Philippe Cudré-Mauroux;\n208 FANCY: Human-centered, Deep Learning-based Framework for Fashion Style Analysis\nHighlight: We present FANCY (Fashion Attributes detectioN for Clustering stYle), a human-centered, deep learning-based framework to support fashion professionals’ analytic tasks using a computational method integrated with their insights. Youngseung Jeon; Seungwan Jin; Kyungsik Han;\n209 PARIMA: Viewport Adaptive 360-Degree Video Streaming\nHighlight: We develop PARIMA, a fast and efficient online viewport prediction model that uses past viewports of users along with the trajectories of prime objects as a representative of video content to predict future viewports. Lovish Chopra; Sarthak Chakraborty; Abhijit Mondal; Sandip Chakraborty;\n210 Controllable and Diverse Text Generation in E-commerce\nHighlight: To remedy these problems, we propose a fine-grained controllable generative model, called Apex, that uses an algorithm borrowed from automatic control (namely, a variant of the proportional, integral, and derivative (PID) controller) to precisely manipulate the diversity/accuracy trade-off of generated text. Huajie Shao; Jun Wang; Haohong Lin; Xuezhou Zhang; Aston Zhang; Heng Ji; Tarek Abdelzaher;\n211 Nonlinear Higher-Order Label Spreading\nHighlight: Here, we add nonlinearity to label spreading via nonlinear functions involving higher-order network structure, namely triangles in the graph. Francesco Tudisco; Austin R. Benson; Konstantin Prokopchik;\n212 HDMI: High-order Deep Multiplex Infomax\n213 Network of Tensor Time Series\nHighlight: We propose a novel model called Network of Tensor Time Series (NeT3), which is comprised of two modules, including Tensor Graph Convolutional Network (TGCN) and Tensor Recurrent Neural Network (TRNN). Baoyu Jing; Hanghang Tong; Yada Zhu;\n214 Improving Graph Neural Networks with Structural Adaptive Receptive Fields\nHighlight: In this paper, we propose Graph Neural Networks with STructural Adaptive Receptive fields (STAR-GNN), which adaptively construct a receptive field for each node with structural information and further achieve better aggregation of information. Xiaojun Ma; Junshan Wang; Hanyue Chen; Guojie Song;\n215 Few-shot Network Anomaly Detection Via Cross-network Meta-learning\nHighlight: Taking advantage of this potential, in this work, we tackle the problem of few-shot network anomaly detection by (1) proposing a new family of graph neural networks – Graph Deviation Networks (GDN) that can leverage a small number of labeled anomalies for enforcing statistically significant deviations between abnormal and normal nodes on a network; and (2) equipping the proposed GDN with a new cross-network meta-learning algorithm to realize few-shot network anomaly detection by transferring meta-knowledge from multiple auxiliary networks. Kaize Ding; Qinghai Zhou; Hanghang Tong; Huan Liu;\n216 Learning Dynamic User Behavior Based on Error-driven Event Representation\nHighlight: Thus in this paper, we provide a new dynamic network dataset with evolving labels called Arxiv and make it publicly available. Honglian Wang; Peiyan Li; Wujun Tao; Bailin Feng; Junming Shao;\n217 Using Prior Knowledge to Guide BERT’s Attention in Semantic Textual Matching Tasks\nHighlight: We study the problem of incorporating prior knowledge into a deep Transformer-based model, i.e., Bidirectional Encoder Representations from Transformers (BERT), to enhance its performance on semantic textual matching tasks. Tingyu Xia; Yue Wang; Yuan Tian; Yi Chang;\n218 Wait, Let’s Think About Your Purchase Again: A Study on Interventions for Supporting Self-Controlled Online Purchases\nHighlight: In this paper, we conducted an online survey with 118 consumers in their 20s to investigate their impulse buying behaviors and self-control strategies. Yunha Han; Hwiyeon Kim; Hyeshin Chu; Joohee Kim; Hyunwook Lee; Seunghyeong Choe; Dooyoung Jung; Dongil Chung; Bum Chul Kwon; Sungahn Ko;\n219 Online Mobile App Usage As An Indicator of Sleep Behavior and Job Performance\nHighlight: In this work, we demonstrate that people’s everyday interactions with online mobile apps can reveal insights into their job performance in real-world contexts. Chunjong Park; Morelle Arian; Xin Liu; Leon Sasson; Jeffrey Kahn; Shwetak Patel; Alex Mariakakis; Tim Althoff;\n220 Quiz-Style Question Generation for News Stories\nHighlight: Using this dataset, we propose a series of novel techniques for applying large pre-trained Transformer encoder-decoder models, namely PEGASUS and T5, to the tasks of question-answer generation and distractor generation. Adam D. Lelkes; Vinh Q. Tran; Cong Yu;\n221 Large-scale Comb-K Recommendation\nHighlight: To solve the promotion recommendation problem, we propose the comb-K recommendation model, a constrained combinatorial optimization model which seamlessly integrates the selection phase and delivery phase with delicately designed constraints. Houye Ji; Junxiong Zhu; Chuan Shi; Xiao Wang; Bai Wang; Chaoyu Zhang; Zixuan Zhu; Feng Zhang; Yanghua Li;\n222 Dual Side Deep Context-aware Modulation for Social Recommendation\nHighlight: To address the above issues, we proposed DICER (Dual sIde deepContext-awarEmodulation for socialRecommendation). Bairan Fu; Wenming Zhang; Guangneng Hu; Xinyu Dai; Shujian Huang; Jiajun Chen;\n223 Graph Neural Networks for Friend Ranking in Large-scale Social Platforms\nHighlight: We study the application of GNNs for friend suggestion, providing the first investigation of GNN design for this task, to our knowledge. Aravind Sankar; Yozen Liu; Jun Yu; Neil Shah;\n224 Pathfinder Discovery Networks for Neural Message Passing\nHighlight: In this work we propose Pathfinder Discovery Networks (PDNs), a method for jointly learning a message passing graph over a multiplex network with a downstream semi-supervised model. Benedek Rozemberczki; Peter Englert; Amol Kapoor; Martin Blais; Bryan Perozzi;\n225 Few-Shot Graph Learning for Molecular Property Prediction\nHighlight: To this end, we propose Meta-MGNN, a novel model for few-shot molecular property prediction. Zhichun Guo; Chuxu Zhang; Wenhao Yu; John Herr; Olaf Wiest; Meng Jiang; Nitesh V. Chawla;\n226 Multi-domain Dialogue State Tracking with Recursive Inference\nHighlight: Here, we propose a Recursive Inference mechanism (ReInf) to resolve DST in multi-domain scenarios that call for more robust and accurate tracking capability. Lizi Liao; Tongyao Zhu; Le Hong Long; Tat Seng Chua;\n227 Automatic Intent-Slot Induction for Dialogue Systems\nHighlight: To tackle these challenges, we explore a new task of automatic intent-slot induction and propose a novel domain-independent tool. Zengfeng Zeng; Dan Ma; Haiqin Yang; Zhen Gou; Jianping Shen;\n228 Multilingual COVID-QA: Learning Towards Global Information Sharing Via Web Question Answering in Multiple Languages\nHighlight: In this paper, we propose a multilingual COVID-QA model to answer people’s questions in their own languages while the model is able to absorb knowledge from other languages. Rui Yan; Weiheng Liao; Jianwei Cui; Hailei Zhang; Yichuan Hu; Dongyan Zhao;\n229 ComQA: Compositional Question Answering Via Hierarchical Graph Neural Networks\nHighlight: In this paper, we present a large-scale compositional question answering dataset containing more than 120k human-labeled questions. Bingning Wang; Ting Yao; Weipeng Chen; Jingfang Xu; Xiaochuan Wang;\n230 Cross-domain Knowledge Distillation for Retrieval-based Question Answering Systems\nHighlight: Hence in this work, we provide a new perspective on the potential of the teacher-student paradigm facilitating cross-domain transfer learning, where the teacher and student tasks belong to heterogeneous domains, with the goal to improve the student model’s performance in the target domain. Cen Chen; Chengyu Wang; Minghui Qiu; Dehong Gao; Linbo Jin; Wang Li;\n231 Computing Views of OWL Ontologies for The Semantic Web\nHighlight: In this paper, we present a forgetting-based method for computing views of OWL ontologies specified in the description logic , the basic extended with role hierarchy, nominals and inverse roles. Jiaqi Li; Xuan Wu; Chang Lu; Wenxing Deng; Yizheng Zhao;\n232 Advanced Semantics for Commonsense Knowledge Extraction\nHighlight: This paper presents a methodology, called Ascent, to automatically build a large-scale knowledge base (KB) of CSK assertions, with advanced expressiveness and both better precision and recall than prior works. Tuan-Phong Nguyen; Simon Razniewski; Gerhard Weikum;\n233 DISCOS: Bridging The Gap Between Discourse Knowledge and Commonsense Knowledge\nHighlight: To address the limitations of previous approaches, in this paper, we propose an alternative commonsense knowledge acquisition framework DISCOS (from DIScourse to COmmonSense), which automatically populates expensive complex commonsense knowledge to more affordable linguistic knowledge resources. Tianqing Fang; Hongming Zhang; Weiqi Wang; Yangqiu Song; Bin He;\n234 Role-Aware Modeling for N-ary Relational Knowledge Bases\nHighlight: Therefore, we start from the role level, and propose a Role-Aware Modeling, RAM for short, for facts in n-ary relational KBs. Yu Liu; Quanming Yao; Yong Li;\n235 Biomedical Vocabulary Alignment at Scale In the UMLS Metathesaurus\nHighlight: This paper aims to improve the UMLS Metathesaurus construction process by developing a novel supervised learning approach for improving the task of suggesting synonymous pairs that can scale to the size and diversity of the UMLS source vocabularies. Vinh Nguyen; Hong Yung Yip; Olivier Bodenreider;\n236 Towards A Lightweight, Hybrid Approach for Detecting DOM XSS Vulnerabilities with Machine Learning\nHighlight: We experiment with a range of hyperparameters and present a low-latency, high-recall classifier that could serve as a pre-filter to taint tracking, reducing the cost of stand-alone taint tracking by 3.43 × while detecting 94.5% of unique vulnerabilities. William Melicher; Clement Fung; Lujo Bauer; Limin Jia;\n237 An Empirical Study of Real-World WebAssembly Binaries: Security, Languages, Use Cases\nHighlight: This paper presents a comprehensive empirical study of 8,461 unique WebAssembly binaries gathered from a wide range of sources, including source code repositories, package managers, and live websites. Aaron Hilbig; Daniel Lehmann; Michael Pradel;\n238 Security of Alerting Authorities in The WWW: Measuring Namespaces, DNSSEC, and Web PKI\nHighlight: In this paper, we take a first look at Alerting Authorities (AA) in the US and investigate security measures related to trustworthy and secure communication. Pouyan Fotouhi Tehrani; Eric Osterweil; Jochen H. Schiller; Thomas C. Schmidt; Matthias Wählisch;\n239 LChecker: Detecting Loose Comparison Bugs in PHP\nHighlight: In this paper, we present the first in-depth study of such loose comparison bugs. Penghui Li; Wei Meng;\n240 SEPAL: Towards A Large-scale Analysis of SEAndroid Policy Customization\nHighlight: To investigate the status quo of SEAndroid policy customization, we propose SEPAL, a universal tool to automatically retrieve and examine the customized policy rules. Dongsong Yu; Guangliang Yang; Guozhu Meng; Xiaorui Gong; Xiu Zhang; Xiaobo Xiang; Xiaoyu Wang; Yue Jiang; Kai Chen; Wei Zou; Wenke Lee; Wenchang Shi;\n241 From Personal Data to Digital Legacy: Exploring Conflicts in The Sharing, Security and Privacy of Post-mortem Data\nHighlight: We report on two workshops carried out with users of password managers to explore their views on the post-mortem sharing, security and privacy of a range of common digital assets. Jack Holt; James Nicholson; Jan David Smeddinck;\n242 ConceptGuide: Supporting Online Video Learning with Concept Map-based Recommendation of Learning Path\nHighlight: In this paper, we present ConceptGuide, a prototype system for learning orientations to support ad hoc online learning from unorganized video materials. Chien-Lin Tang; Jingxian Liao; Hao-Chuan Wang; Ching-Ying Sung; Wen-Chieh Lin;\n243 An Experimental Study to Understand User Experience and Perception Bias Occurred By Fact-checking Messages\nHighlight: This research brings attention to the unexpected and diminished effect of fact-checking due to cognitive biases. Sungkyu Park; Jamie Yejean Park; Hyojin Chin; Jeong-han Kang; Meeyoung Cha;\n244 Touch Screen Exploration of Visual Artwork for Blind People\nHighlight: This paper investigates how touchscreen exploration and verbal feedback can be used to support blind people to access visual artwork. Dragan Ahmetovic; Nahyun Kwon; Uran Oh; Cristian Bernareggi; Sergio Mascetti;\n245 Generating Accurate Caption Units for Figure Captioning\nHighlight: We formulate the caption unit generation problem as a controlled captioning problem. Given a caption unit type as a control signal, a model generates an accurate caption unit of that type. Xin Qian; Eunyee Koh; Fan Du; Sungchul Kim; Joel Chan; Ryan A. Rossi; Sana Malik; Tak Yeon Lee;\n246 Stochastic Bandits for Multi-platform Budget Optimization in Online Advertising\nHighlight: We study the problem of an online advertising system that wants to optimally spend an advertiser’s given budget for a campaign across multiple platforms, without knowing the value for showing an ad to the users on those platforms. Vashist Avadhanula; Riccardo Colini Baldeschi; Stefano Leonardi; Karthik Abinav Sankararaman; Okke Schrijvers;\n247 Incrementality Testing in Programmatic Advertising: Enhanced Precision with Double-Blind Designs\nHighlight: We present a novel randomized design solution for incrementality testing based on ghost bidding with improved measurement precision. Joel Barajas; Narayan Bhamidipati;\n248 FM2: Field-matrixed Factorization Machines for Recommender Systems\nHighlight: In this paper, we proposed a novel approach to model the field information effectively and efficiently. Yang Sun; Junwei Pan; Alex Zhang; Aaron Flores;\n249 Integrating Floor Plans Into Hedonic Models for Rent Price Appraisal\nHighlight: In this study, we investigate to what extent an automated visual analysis of apartment floor plans on online real estate platforms can enhance hedonic rent price appraisal. Kirill Solovev; Nicolas Pröllochs;\n250 TextGNN: Improving Text Encoder Via Graph Neural Network in Sponsored Search\nHighlight: We propose the TextGNN model that naturally extends the strong twin tower structured encoders with the complementary graph information from user historical behaviors, which serves as a natural guide to help us better understand the intents and hence generate better language representations. Jason Zhu; Yanling Cui; Yuming Liu; Hao Sun; Xue Li; Markus Pelger; Tianqi Yang; Liangjie Zhang; Ruofei Zhang; Huasha Zhao;\n251 Leveraging User Behavior History for Personalized Email Search\nHighlight: Accordingly, we propose a context-dependent neural ranking model (CNRM) that encodes the ranking features in users’ search history as query context and show that it can significantly outperform the baseline neural model without using the context. Keping Bi; Pavel Metrikov; Chunyuan Li; Byungki Byun;\n252 Partial-Softmax Loss Based Deep Hashing\nHighlight: To overcome this difficulty, in this paper, we propose a novel Partial-Softmax Loss based Deep Hashing, called PSLDH, to generate high-quality hash codes. Rong-Cheng Tu; Xian-Ling Mao; Jia-Nan Guo; Wei Wei; Heyan Huang;\n253 Unsupervised Multi-Index Semantic Hashing\nHighlight: In this work, we propose Multi-Index Semantic Hashing (MISH), an unsupervised hashing model that learns hash codes that are both effective and highly efficient by being optimized for multi-index hashing. Christian Hansen; Casper Hansen; Jakob Grue Simonsen; Stephen Alstrup; Christina Lioma;\n254 Learning A Product Relevance Model from Click-Through Data in E-Commerce\nHighlight: In this paper, we propose a new relevance learning framework that concentrates on how to train a relevance model from the weak supervision of click-through data. Shaowei Yao; Jiwei Tan; Xi Chen; Keping Yang; Rong Xiao; Hongbo Deng; Xiaojun Wan;\n255 High-Dimensional Sparse Cross-Modal Hashing with Fine-Grained Similarity Embedding\nHighlight: Motivated by these issues, in this paper, we present an efficient sparse hashing method, i.e., High-dimensional Sparse Cross-modal Hashing, HSCH for short. Yongxin Wang; Zhen-Duo Chen; Xin Luo; Xin-Shun Xu;\n256 Hashing-Accelerated Graph Neural Networks for Link Prediction\nHighlight: In this paper, we propose a simple and effective model called #GNN, which balances the trade-off between accuracy and efficiency. Wei Wu; Bin Li; Chuan Luo; Wolfgang Nejdl;\n257 Multi-view Graph Contrastive Representation Learning for Drug-Drug Interaction Prediction\nHighlight: This study presents a new method, multi-view graph contrastive representation learning for drug-drug interaction prediction, MIRACLE for brevity, to capture inter-view molecule structure and intra-view interactions between molecules simultaneously. Yingheng Wang; Yaosen Min; Xin Chen; Ji Wu;\n258 Multi-level Hyperedge Distillation for Social Linking Prediction on Sparsely Observed Networks\nHighlight: To this end, we go beyond pair-wise relations and propose a new and novel framework using hypergraph neural networks with multi-level hyperedge distillation strategies. Xiangguo Sun; Hongzhi Yin; Bo Liu; Hongxu Chen; Qing Meng; Wang Han; Jiuxin Cao;\n259 Self-Supervised Learning of Contextual Embeddings for Link Prediction in Heterogeneous Networks\nHighlight: To tackle this challenge, we develop , a framework for bridging static representation learning methods using global information from the entire graph with localized attention driven mechanisms to learn contextual node representations. Ping Wang; Khushbu Agarwal; Colby Ham; Sutanay Choudhury; Chandan K. Reddy;\n260 Community Value Prediction in Social E-commerce\nHighlight: To bridge this gap, we develop a Multi-scale Structure-aware Community value prediction network (MSC) that jointly models the structural information of different scales, including peer relations, community structure, and inter-community connections, to predict the value of given communities. Guozhen Zhang; Yong Li; Yuan Yuan; Fengli Xu; Hancheng Cao; Yujian Xu; Depeng Jin;\n261 RetaGNN: Relational Temporal Attentive Graph Neural Networks for Holistic Sequential Recommendation\nHighlight: In this work, we aim to present the holistic SR that simultaneously accommodates conventional, inductive, and transferable settings. Cheng Hsu; Cheng-Te Li;\n262 Disentangling User Interest and Conformity for Recommendation with Causal Embedding\nHighlight: In this paper, we present DICE, a general framework that learns representations where interest and conformity are structurally disentangled, and various backbone recommendation models could be smoothly integrated. Yu Zheng; Chen Gao; Xiang Li; Xiangnan He; Yong Li; Depeng Jin;\n263 Future-Aware Diverse Trends Framework for Recommendation\nHighlight: In this paper, we bridge the gap between the past preference and potential future preference by proposing the future-aware diverse trends (FAT) framework. Yujie Lu; Shengyu Zhang; Yingxuan Huang; Luyao Wang; Xinyao Yu; Zhou Zhao; Fei Wu;\n264 Graph Embedding for Recommendation Against Attribute Inference Attacks\nHighlight: In our paper, we propose GERAI, a novel differentially private graph convolutional network to address such limitations. Shijie Zhang; Hongzhi Yin; Tong Chen; Zi Huang; Lizhen Cui; Xiangliang Zhang;\n265 AutoDim: Field-aware Embedding Dimension Searchin Recommender Systems\nHighlight: To this end, we propose an AutoML-based framework (AutoDim) in this paper, which can automatically select dimensions for different feature fields in a data-driven fashion. Xiangyu Zhao; Haochen Liu; Hui Liu; Jiliang Tang; Weiwei Guo; Jun Shi; Sida Wang; Huiji Gao; Bo Long;\n266 Verdi: Quality Estimation and Error Detection for Bilingual Corpora\nHighlight: In this paper, we propose Verdi, a novel framework for word-level and sentence-level post-editing effort estimation for bilingual corpora. Mingjun Zhao; Haijiang Wu; Di Niu; Zixuan Wang; Xiaoli Wang;\n267 Crosslingual Topic Modeling with WikiPDA\nHighlight: We present Wikipedia-based Polyglot Dirichlet Allocation (WikiPDA), a crosslingual topic model that learns to represent Wikipedia articles written in any language as distributions over a common set of language-independent topics. Tiziano Piccardi; Robert West;\n268 Keyword-aware Abstractive Summarization By Extracting Set-level Intermediate Summaries\nHighlight: In this paper, we propose a extractor-abstractor framework in which the keyword-based extractor selects a few sets of salient sentences from the input document and then the abstractor paraphrases these sets of sentences in parallel, which are more aligned to the summary, to generate the final summary. Yizhu Liu; Qi Jia; Kenny Zhu;\n269 Graph Topic Neural Network for Document Representation\nHighlight: In this paper, we propose a novel Graph Topic Neural Network (GTNN) model to mine latent topic semantics for interpretable document representation learning, taking into account the document-document, document-word, and word-word relationships in the graph. Qianqian Xie; Jimin Huang; Pan Du; Min Peng; Jian-Yun Nie;\n270 Insightful Dimensionality Reduction with Very Low Rank Variable Subsets\nHighlight: In this paper we explore an alternative approach to interpretable dimensionality reduction. Bruno Ordozgoiti; Sachith Pai; Marta Kołczyńska;\n271 SDFVAE: Static and Dynamic Factorized VAE for Anomaly Detection of Multivariate CDN KPIs\nHighlight: In this paper, we propose a robust and noise-resilient anomaly detection mechanism using multivariate KPIs. Liang Dai; Tao Lin; Chang Liu; Bo Jiang; Yanwei Liu; Zhen Xu; Zhi-Li Zhang;\n272 MicroRank: End-to-End Latency Issue Localization with Extended Spectrum Analysis in Microservice Environments\nHighlight: This paper proposes a novel system, named MicroRank, which analyzes clues provided by normal and abnormal traces to locate root causes of latency issues. Guangba Yu; Pengfei Chen; Hongyang Chen; Zijie Guan; Zicheng Huang; Linxiao Jing; Tianjun Weng; Xinmeng Sun; Xiaoyun Li;\n273 Outlier-Resilient Web Service QoS Prediction\nHighlight: To overcome this limitation, we propose an outlier-resilient QoS prediction method in this paper. Fanghua Ye; Zhiwei Lin; Chuan Chen; Zibin Zheng; Hong Huang;\n274 Autodidactic Neurosurgeon: Collaborative Deep Inference for Mobile Edge Intelligence Via Online Learning\nHighlight: This paper builds a collaborative deep inference system between a resource-constrained mobile device and a powerful edge server, aiming at joining the power of both on-device processing and computation offloading. Letian Zhang; Lixing Chen; Jie Xu;\n275 Time Series Change Point Detection with Self-Supervised Contrastive Predictive Coding\nHighlight: We propose a novel approach for self-supervised Time Series Change Point detection method based on Contrastive Predictive coding (TS ? CP2). Shohreh Deldari; Daniel V. Smith; Hao Xue; Flora D. Salim;\n276 REST: Reciprocal Framework for Spatiotemporal-coupled Predictions\nHighlight: To raise the bar, this paper proposes to jointly mine the spatial dependencies and model temporal patterns in a coupled framework, i.e., to make spatiotemporal-coupled predictions. Haozhe Lin; Yushun Fan; Jia Zhang; Bing Bai;\n277 Predicting Customer Value with Social Relationships Via Motif-based Graph Attention Networks\nHighlight: In this work, we fill this gap with a novel framework — Motif-based Multi-view Graph Attention Networks with Gated Fusion (MAG), which jointly considers customer demographics, past behaviors, and social network structures. Jinghua Piao; Guozhen Zhang; Fengli Xu; Zhilong Chen; Yong Li;\n278 HINTS: Citation Time Series Prediction for New Publications Via Dynamic Heterogeneous Information Network Embedding\nHighlight: In this paper, we tackle a new problem: predicting a new paper’s citation time series from the date of publication (i.e., without leading values). Song Jiang; Bernard Koch; Yizhou Sun;\n279 Pick and Choose: A GNN-based Imbalanced Learning Approach for Fraud Detection\nHighlight: To remedy the class imbalance problem of graph-based fraud detection, we propose a Pick and Choose Graph Neural Network (PC-GNN for short) for imbalanced supervised learning on graphs. Yang Liu; Xiang Ao; Zidi Qin; Jianfeng Chi; Jinghua Feng; Hao Yang; Qing He;\n280 Rumor Detection with Field of Linear and Non-Linear Propagation\nHighlight: Accordingly, in this paper, we propose a novel model Rumor Detection with Field of Linear and Non-Linear Propagation (RDLNP) to automatically detect rumors from the above two fields by taking advantage of claim content, social context and temporal information. An Lao; Chongyang Shi; Yayi Yang;\n281 Situation and Behavior Understanding By Trope Detection on Films\nHighlight: Here, we present a challenging novel task, trope detection on films, in an effort to create a situation and behavior understanding for machines. Chen-Hsi Chang; Hung-Ting Su; Jui-Heng Hsu; Yu-Siang Wang; Yu-Cheng Chang; Zhe Yu Liu; Ya-Liang Chang; Wen-Feng Cheng; Ke-Jyun Wang; Winston H. Hsu;\n282 A Novel Macro-Micro Fusion Network for User Representation Learning on Mobile Apps\nHighlight: In this paper, we study the task of user representation learning with both macro and micro interaction data on mobile apps. Shuqing Bian; Wayne Xin Zhao; Kun Zhou; Xu Chen; Jing Cai; Yancheng He; Xingji Luo; Ji-Rong Wen;\n283 Where To Next? A Dynamic Model of User Preferences\nHighlight: In this paper, we argue that these models fail to anticipate changing preferences. Francesco Sanna Passino; Lucas Maystre; Dmitrii Moor; Ashton Anderson; Mounia Lalmas;\n284 Density-Ratio Based Personalised Ranking From Implicit Feedback\nHighlight: This study aims to establish an approach to learn personalised ranking from implicit feedback, which reconciles the training efficiency of the pointwise approach and ranking effectiveness of the pairwise counterpart. Riku Togashi; Masahiro Kato; Mayu Otani; Shin’ichi Satoh;\n285 Itinerary-aware Personalized Deep Matching at Fliggy\nHighlight: In this paper, we present a novel Fliggy ITinerary-aware deep matching NETwork (FitNET) to address these three challenges. Jia Xu; Ziyi Wang; Zulong Chen; Detao Lv; Yao Yu; Chuanfei Xu;\n286 MATCH: Metadata-Aware Text Classification in A Large Hierarchy\nHighlight: In this paper, we bridge the gap by formalizing the problem of metadata-aware text classification in a large label hierarchy (e.g., with tens of thousands of labels). Yu Zhang; Zhihong Shen; Yuxiao Dong; Kuansan Wang; Jiawei Han;\n287 Minimally-Supervised Structure-Rich Text Categorization Via Learning on Text-Rich Networks\nHighlight: We therefore propose a novel framework for minimally supervised categorization by learning from the text-rich network. Xinyang Zhang; Chenwei Zhang; Xin Luna Dong; Jingbo Shang; Jiawei Han;\n288 Scalable Auto-weighted Discrete Multi-view Clustering\nHighlight: In this paper, we propose a novel approach for large-scale multi-view clustering to overcome the above challenges. Longqi Yang; Liangliang Zhang; Yuhua Tang;\n289 Linguistically-Enriched and Context-AwareZero-shot Slot Filling\nHighlight: We propose a new zero-shot slot filling neural model, , which works in three steps. A.B. Siddique; Fuad Jamour; Vagelis Hristidis;\n290 Enquire One’s Parent and Child Before Decision: Fully Exploit Hierarchical Structure for Self-Supervised Taxonomy Expansion\nHighlight: In this paper, we propose the Hierarchy Expansion Framework (HEF), which fully exploits the hierarchical structure’s properties to maximize the coherence of expanded taxonomy. Suyuchen Wang; Ruihui Zhao; Xi Chen; Yefeng Zheng; Bang Liu;\n291 Typing Errors in Factual Knowledge Graphs: Severity and Possible Ways Out\nHighlight: In this study, we investigate the quality of these KGs, where the typing error rate is estimated to be 27% for coarse-grained types on average, and even 73% for certain fine-grained types. Peiran Yao; Denilson Barbosa;\n292 Few-Shot Knowledge Validation Using Rules\nHighlight: To address these issues, we propose Colt, a few-shot rule-based knowledge validation framework that enables the interactive quality assessment of logic rules. Michael Loster; Davide Mottin; Paolo Papotti; Jan Ehmüller; Benjamin Feldmann; Felix Naumann;\n293 OntoZSL: Ontology-enhanced Zero-shot Learning\nHighlight: In this paper, we explore richer and more competitive prior knowledge to model the inter-class relationship for ZSL via ontology-based knowledge representation and semantic embedding. Yuxia Geng; Jiaoyan Chen; Zhuo Chen; Jeff Z. Pan; Zhiquan Ye; Zonggang Yuan; Yantao Jia; Huajun Chen;\n294 Trav-SHACL: Efficiently Validating Networks of SHACL Constraints\nHighlight: We present Trav-SHACL, a SHACL engine capable of planning the traversal and execution of a shape schema in a way that invalid entities are detected early and needless validations are minimized. Mónica Figuera; Philipp D. Rohde; Maria-Esther Vidal;\n295 Online Disease Diagnosis with Inductive Heterogeneous Graph Convolutional Networks\nHighlight: We propose a Healthcare Graph Convolutional Network (HealGCN) to offer disease self-diagnosis service for online users based on Electronic Healthcare Records (EHRs). Zifeng Wang; Rui Wen; Xi Chen; Shilei Cao; Shao-Lun Huang; Buyue Qian; Yefeng Zheng;\n296 Causal Network Motifs: Identifying Heterogeneous Spillover Effects in A/B Tests\nHighlight: Our study provides an approach that accounts for both the local structure in a user’s social network via motifs as well as the treatment assignment conditions of neighbors. Yuan Yuan; Kristen Altenburger; Farshad Kooti;\n297 MStream: Fast Anomaly Detection in Multi-Aspect Streams\nHighlight: Our work aims to define a streaming multi-aspect data anomaly detection framework, termed MStream which can detect unusual group anomalies as they occur, in a dynamic manner. Siddharth Bhatia; Arjit Jain; Pan Li; Ritesh Kumar; Bryan Hooi;\n298 Knowledge-Preserving Incremental Social Event Detection Via Heterogeneous GNNs\nHighlight: In this paper, we propose a novel Knowledge-Preserving Incremental Heterogeneous Graph Neural Network (KPGNN) for incremental social event detection. Yuwei Cao; Hao Peng; Jia Wu; Yingtong Dou; Jianxin Li; Philip S. Yu;\n299 How Do Hyperedges Overlap in Real-World Hypergraphs? – Patterns, Measures, and Generators\nHighlight: In this work, we closely investigate thirteen real-world hypergraphs from various domains and share interesting observations of the overlaps of hyperedges. Geon Lee; Minyoung Choe; Kijung Shin;\n300 STruD: Truss Decomposition of Simplicial Complexes\nHighlight: In this paper, we develop a new tool to study the structure of simplicial complexes: we generalize the graph notion of truss decomposition to complexes, and show that this more powerful representation gives rise to different properties compared to the graph-based one. Giulia Preti; Gianmarco De Francisci Morales; Francesco Bonchi;\n301 Constructing Explainable Opinion Graphs from Reviews\nHighlight: We present ExplainIt, a system that extracts and organizes opinions into an opinion graph, which are useful for downstream applications such as generating explainable review summaries and facilitating search over opinion phrases. Nofar Carmeli; Xiaolan Wang; Yoshihiko Suhara; Stefanos Angelidis; Yuliang Li; Jinfeng Li; Wang-Chiew Tan;\n302 The Surprising Performance of Simple Baselines for Misinformation Detection\nHighlight: In this paper, we examine the performance of a broad set of modern transformer-based language models and show that with basic fine-tuning, these models are competitive with and can even significantly outperform recently proposed state-of-the-art methods. Kellin Pelrine; Jacob Danovitch; Reihaneh Rabbany;\n303 MQuadE: A Unified Model for Knowledge Fact Embedding\nHighlight: This paper proposes a method called MQuadE to tackle the challenge in KGE modeling. Jinxing Yu; Yunfeng Cai; Mingming Sun; Ping Li;\n304 Target-adaptive Graph for Cross-target Stance Detection\nHighlight: This paper presents a novel approach that automatically identifies and adapts the target-dependent and target-independent roles that a word plays with respect to a specific target in stance expressions, so as to achieve cross-target stance detection. Bin Liang; Yonghao Fu; Lin Gui; Min Yang; Jiachen Du; Yulan He; Ruifeng Xu;\n305 Mining Dual Emotion for Fake News Detection\nHighlight: In this paper, we verify that dual emotion is distinctive between fake and real news and propose Dual Emotion Features to represent dual emotion and the relationship between them for fake news detection. Xueyao Zhang; Juan Cao; Xirong Li; Qiang Sheng; Lei Zhong; Kai Shu;\n306 Beyond I.I.D.: Three Levels of Generalization for Question Answering on Knowledge Bases\nHighlight: Instead, we suggest that KBQA models should have three levels of built-in generalization: i.i.d., compositional, and zero-shot. Yu Gu; Sue Kase; Michelle Vanni; Brian Sadler; Percy Liang; Xifeng Yan; Yu Su;\n307 Improving Neural Question Generation Using Deep Linguistic Representation\nHighlight: In this work, inspired by the recent achievements of text representation, we propose to utilize linguistic information via large pre-trained neural models. Wei Yuan; Tieke He; Xinyu Dai;\n308 Diverse and Specific Clarification Question Generation with Keywords\nHighlight: We propose a new model named KPCNet, which generates CQs with Keyword Prediction and Conditioning, to deal with the tasks. Zhiling Zhang; Kenny Zhu;\n309 Knowledge-Aware Procedural Text Understanding with Multi-Stage Training\nHighlight: In this work, we focus on the task of procedural text understanding, which aims to comprehend such documents and track entities’ states and locations during a process. Zhihan Zhang; Xiubo Geng; Tao Qin; Yunfang Wu; Daxin Jiang;\n310 Multi-level Connection Enhanced Representation Learning for Script Event Prediction\nHighlight: Here we propose a novel framework which can enhance the representation learning of events by mining their connections at multiple granularity levels, including argument level, event level and chain level. Lihong Wang; Juwei Yue; Shu Guo; Jiawei Sheng; Qianren Mao; Zhenyu Chen; Shenghai Zhong; Chen Li;\n311 Unsupervised Lifelong Learning with Curricula\nHighlight: Motivated by this situation, we propose a new paradigm named unsupervised lifelong learning with curricula (ULLC), where only one task needs to be labeled for initialization and the system then performs lifelong learning for subsequent tasks in an unsupervised fashion. Yi He; Sheng Chen; Baijun Wu; Xu Yuan; Xindong Wu;\n312 Taxonomy-aware Learning for Few-Shot Event Detection\nHighlight: To deal with the low-resources problem, we investigate few-shot event detection in this paper and propose TaLeM, a novel taxonomy-aware learning model, consisting of two components, i.e., the taxonomy-aware self-supervised learning framework (TaSeLF) and the taxonomy-aware prototypical networks (TaPN). Jianming Zheng; Fei Cai; Wanyu Chen; Wengqiang Lei; Honghui Chen;\n313 Distilling Knowledge from Publicly Available Online EMR Data to Emerging Epidemic for Prognosis\nHighlight: In this paper, we propose a distilled transfer learning framework, which leverages the existing publicly available online Electronic Medical Records to enhance the prognosis for inpatients with emerging infectious diseases. Liantao Ma; Xinyu Ma; Junyi Gao; Xianfeng Jiao; Zhihao Yu; Chaohe Zhang; Wenjie Ruan; Yasha Wang; Wen Tang; Jiangtao Wang;\n314 AID: Active Distillation Machine to Leverage Pre-Trained Black-Box Models in Private Data Settings\nHighlight: This paper presents an active distillation method for a local institution (e.g., hospital) to find the best queries within its given budget to distill an on-server black-box model’s predictive knowledge into a local surrogate with transparent parameterization. Trong Nghia Hoang; Shenda Hong; Cao Xiao; Bryan Low; Jimeng Sun;\n315 UserSim: User Simulation Via Supervised GenerativeAdversarial Network\nHighlight: In this paper, we develop a user simulator based on a Generative Adversarial Network (GAN). Xiangyu Zhao; Long Xia; Lixin Zou; Hui Liu; Dawei Yin; Jiliang Tang;\n316 Adversarial Item Promotion: Vulnerabilities at The Core of Top-N Recommenders That Use Images to Address Cold Start\nHighlight: In this paper, we demonstrate how unscrupulous merchants can create item images that artificially promote their products, improving their rankings. Zhuoran Liu; Martha Larson;\n317 Robust Android Malware Detection Against Adversarial Example Attacks\nHighlight: In this paper, we propose a novel robust Android malware detection approach that can resist adversarial examples without requiring their instances or knowledge by jointly investigating malware detection and adversarial example defenses. Heng Li; Shiyao Zhou; Wei Yuan; Xiapu Luo; Cuiying Gao; Shuiyan Chen;\n318 Where Are You Taking Me?Understanding Abusive Traffic Distribution Systems\nHighlight: We design ODIN (Observatory of Dynamic Illicit ad Networks), the first system to study cloaking, user differentiation and business integration at the same time in four different types of traffic sources: typosquatting, copyright-infringing movie streaming, ad-based URL shortening, and illicit online pharmacy websites. Janos Szurdi; Meng Luo; Brian Kondracki; Nick Nikiforakis; Nicolas Christin;\n319 One Detector to Rule Them All: Towards A General Deepfake Attack Detection Framework\nHighlight: To better cope with unknown and unseen deepfakes, we introduce a Convolutional LSTM-based Residual Network (CLRNet), which adopts a unique model training strategy and explores spatial as well as the temporal information in a deepfakes. Shahroz Tariq; Sangyup Lee; Simon Woo;\n320 A Targeted Attack on Black-Box Neural Machine Translation with Parallel Data Poisoning\nHighlight: In this paper, we show that targeted attacks on black-box NMT systems are feasible, based on poisoning a small fraction of their parallel training data. Chang Xu; Jun Wang; Yuqing Tang; Francisco Guzmán; Benjamin I. P. Rubinstein; Trevor Cohn;\n321 On The Equivalence of Decoupled Graph Convolution Network and Label Propagation\nHighlight: In this paper, we explore the decoupled GCN for semi-supervised node classification from a novel and fundamental perspective — label propagation. Hande Dong; Jiawei Chen; Fuli Feng; Xiangnan He; Shuxian Bi; Zhaolin Ding; Peng Cui;\n322 Mixup for Node and Graph Classification\nHighlight: In this paper, we propose the Mixup methods for two fundamental tasks in graph learning: node and graph classification. Yiwei Wang; Wei Wang; Yuxuan Liang; Yujun Cai; Bryan Hooi;\n323 Effective and Scalable Clustering on Massive Attributed Graphs\nHighlight: In this paper, we propose , an efficient approach to k-AGC that yields high-quality clusters with costs linear to the size of the input graph G. Renchi Yang; Jieming Shi; Yin Yang; Keke Huang; Shiqi Zhang; Xiaokui Xiao;\n324 Mask-GVAE: Blind Denoising Graphs Via Partition\nHighlight: We present Mask-GVAE, a variational generative model for blind denoising large discrete graphs, in which ”blind denoising” means we don’t require any supervision from clean graphs. Jia Li; Mengzhou Liu; Honglei Zhang; Pengyun Wang; Yong Wen; Lujia Pan; Hong Cheng;\n325 Bridging The Gap Between Von Neumann Graph Entropy and Structural Information: Theory and Applications\nHighlight: In this work, we thereby study the difference between the structural information and VNGE named as entropy gap. Xuecheng Liu; Luoyi Fu; Xinbing Wang;\n326 Convex Surrogates for Unbiased Loss Functions in Extreme Classification With Missing Labels\nHighlight: In this work, we consider common loss functions that decompose over labels, and calculate unbiased estimates that compensate missing labels according to Natarajan et al. . Mohammadreza Qaraei; Erik Schultheis; Priyanshu Gupta; Rohit Babbar;\n327 ECLARE: Extreme Classification with Label Graph Correlations\nHighlight: This paper presents ECLARE, a scalable deep learning architecture that incorporates not only label text, but also label correlations, to offer accurate real-time predictions within a few milliseconds. Anshul Mittal; Noveen Sachdeva; Sheshansh Agrawal; Sumeet Agarwal; Purushottam Kar; Manik Varma;\n328 GalaXC: Graph Neural Networks with Labelwise Attention for Extreme Classification\nHighlight: To remedy these, GalaXC presents a framework that enables collaborative learning over joint document-label graphs at massive scales, in a way that naturally allows various auxiliary sources of information, including label metadata, to be incorporated. Deepak Saini; Arnav Kumar Jain; Kushal Dave; Jian Jiao; Amit Singh; Ruofei Zhang; Manik Varma;\n329 Generalizing Discriminative Retrieval Models Using Generative Tasks\nHighlight: In this paper, we propose a novel multi-task learning approach which can be used to produce more effective neural ranking models. Binsheng Liu; Hamed Zamani; Xiaolu Lu; J. Shane Culpepper;\n330 FedPS: A Privacy Protection Enhanced Personalized Search Framework\nHighlight: In this paper, we focus on the problem of privacy protection in personalized search, and propose a privacy protection enhanced personalized search framework, denoted with FedPS. Jing Yao; Zhicheng Dou; Ji-Rong Wen;\n331 Auditing for Discrimination in Algorithms Delivering Job Ads\n332 Debiasing Career Recommendations with Neural Fair Collaborative Filtering\nHighlight: In this work, we investigate gender bias in collaborative-filtering recommender systems trained on social media data. Rashidul Islam; Kamrun Naher Keya; Ziqian Zeng; Shimei Pan; James Foulds;\n333 The Interaction Between Political Typology and Filter Bubbles in News Recommendation Algorithms\nHighlight: In this paper, we investigate this phenomenon in the context of political news recommendation algorithms, which have important implications for civil discourse. Ping Liu; Karthik Shivaram; Aron Culotta; Matthew A. Shapiro; Mustafa Bilgic;\n334 Dialect Diversity in Text Summarization on Twitter\nHighlight: To correct for the dialect bias, we employ a framework that takes an existing text summarization algorithm as a blackbox and, using a small set of dialect-diverse sentences, returns a summary that is relatively more dialect-diverse. Vijay Keswani; L. Elisa Celis;\n335 Fairness-Aware PageRank\nHighlight: In this paper, we consider fairness for link analysis and in particular for the celebrated Pagerank algorithm. Sotiris Tsioutsiouliklis; Evaggelia Pitoura; Panayiotis Tsaparas; Ilias Kleftakis; Nikos Mamoulis;\n336 Cost-Effective and Interpretable Job Skill Recommendation with Deep Reinforcement Learning\nHighlight: To this end, in this paper, we aim to develop a cost-effective recommendation system based on deep reinforcement learning, which can provide personalized and interpretable job skill recommendation for each talent. Ying Sun; Fuzhen Zhuang; Hengshu Zhu; Qing He; Hui Xiong;\n337 Personalized Approximate Pareto-Efficient Recommendation\nHighlight: To capture users’ objective-level preferences and enhance personalization in Pareto-efficient recommendation, we propose a novel Personalized Approximate Pareto-Efficient Recommendation (PAPERec) framework for multi-objective recommendation. Ruobing Xie; Yanlei Liu; Shaoliang Zhang; Rui Wang; Feng Xia; Leyu Lin;\n338 ELIXIR: Learning from User Feedback on Explanations To Improve Recommender Models\nHighlight: In this paper, we turn the role of explanations around and investigate how they can contribute to enhancing the quality of generated recommendations themselves. Azin Ghazimatin; Soumajit Pramanik; Rishiraj Saha Roy; Gerhard Weikum;\n339 Bidirectional Distillation for Top-K Recommender System\nHighlight: Based on this observation, we propose Bidirectional Distillation (BD) framework whereby both the teacher and the student collaboratively improve with each other. Wonbin Kweon; Seongku Kang; Hwanjo Yu;\n340 Towards Content Provider Aware Recommender Systems: A Simulation Study on The Interplay Between User and Provider Utilities\nHighlight: This paper aims to serve as a research investigation of one approach toward building a content provider aware recommender, and evaluating its impact in a simulated setup. Ruohan Zhan; Konstantina Christakopoulou; Ya Le; Jayden Ooi; Martin Mladenov; Alex Beutel; Craig Boutilier; Ed Chi; Minmin Chen;\n341 Robust Network Alignment Via Attack Signal Scaling and Adversarial Perturbation Elimination\nHighlight: In this paper, we propose a robust network alignment solution, RNA, for offering preemptive protection of existing network alignment algorithms, enhanced with the guidance of effective adversarial attacks. Yang Zhou; Zeru Zhang; Sixing Wu; Victor Sheng; Xiaoying Han; Zijie Zhang; Ruoming Jin;\n342 Attent: Active Attributed Network Alignment\nHighlight: In this paper, we tackle this challenge and propose an active network alignment method (Attent) to identify the best nodes to query. Qinghai Zhou; Liangyue Li; Xintao Wu; Nan Cao; Lei Ying; Hanghang Tong;\n343 BRIGHT: A Bridging Algorithm for Network Alignment\nHighlight: To overcome these two limitations, we bridge these methods and propose a novel family of network alignment algorithms BRIGHT to handle both plain and attributed networks. Yuchen Yan; Si Zhang; Hanghang Tong;\n344 Sketch-based Algorithms for Approximate Shortest Paths in Road Networks\nHighlight: In this paper, we propose very efficient algorithms, based on a distance oracle, for computing approximate shortest paths and alternate paths in road networks. Gaurav Aggarwal; Sreenivas Gollapudi; Raghavender; Ali Kemal Sinop;\n345 Efficient Reductions and A Fast Algorithm of Maximum Weighted Independent Set\nHighlight: In this paper, we study the structural properties of this problem, giving some sufficient conditions for a vertex being or not being in a maximum weighted independent set. Mingyu Xiao; Sen Huang; Yi Zhou; Bolin Ding;\n346 Auction Design for ROI-Constrained Buyers\n347 Bid Prediction in Repeated Auctions with Learning\nHighlight: We propose econometric approaches to simultaneously learn the parameters of a player’s utility and her learning rule, and apply these methods to a real-world dataset from the BingAds sponsored search auction marketplace. Gali Noti; Vasilis Syrgkanis;\n348 Towards Efficient Auctions in An Auto-bidding World\nHighlight: In this paper, we propose a family of auctions with boosts to improve welfare in auto-bidding environments with both return on ad spend constraints and budget constraints. Yuan Deng; Jieming Mao; Vahab Mirrokni; Song Zuo;\n349 Information Elicitation from Rowdy Crowds\nHighlight: We initiate the study of information elicitation mechanisms for a crowd containing both self-interested agents, who respond to incentives, and adversarial agents, who may collude to disrupt the system. Grant Schoenebeck; Fang-Yi Yu; Yichi Zhang;\n350 Evaluating The Rationales of Amateur Investors\nHighlight: In this paper, we propose an approach for capturing expert-like rationales from social media platforms without the requirement of the annotated data. Chung-Chi Chen; Hen-Hsen Huang; Hsin-Hsi Chen;\n351 Information Extraction From Co-Occurring Similar Entities\nHighlight: In this paper, we explore how information extracted from similar entities that co-occur in structures like tables or lists can help to increase the coverage of such knowledge graphs. Nicolas Heist; Heiko Paulheim;\n352 Unsupervised Semantic Association Learning with Latent Label Inference\nHighlight: In this paper, we unify a diverse set of learning tasks in NLP, semantic retrieval and related areas, under a common umbrella, which we call unsupervised semantic association learning (USAL). Yanzhao Zhang; Richong Zhang; Jaein Kim; Xudong Liu; Yongyi Mao;\n353 TCN: Table Convolutional Network for Web Table Interpretation\nHighlight: In this work, we propose a novel relational table representation learning approach considering both the intra- and inter-table contextual information. Daheng Wang; Prashant Shiralkar; Colin Lockard; Binxuan Huang; Xin Luna Dong; Meng Jiang;\n354 Extracting Contextualized Quantity Facts from Web Tables\nHighlight: To enable such queries over web contents, this paper develops a novel method for automatically extracting quantity facts from ad-hoc web tables. Vinh Thinh Ho; Koninika Pal; Simon Razniewski; Klaus Berberich; Gerhard Weikum;\n355 Searching to Sparsify Tensor Decomposition for N-ary Relational Data\nHighlight: In this paper, we propose a novel method, i.e., S2S, for effectively and efficiently learning from the N-ary relational data. Shimin Di; Quanming Yao; Lei Chen;