Bitcoin is a virtual coinage system that enables users to trade virtually free of a central trusted authority. All transactions on the Bitcoin blockchain are publicly available for viewing, yet as Bitcoin is built mainly for security it’s original structure does not allow for direct analysis of address transactions. 
 Existing analysis methods of the Bitcoin blockchain can be complicated, computationally expensive or inaccurate. We propose a computationally efficient model to analyze bitcoin blockchain addresses and allow for their use with existing machine learning algorithms. We compare our approach against Multi Level Sequence Learners (MLSLs), one of the best performing models on bitcoin address data. Bitcoin(Nakamoto) is a virtual coinage system that functions much like a standard currency, enabling users to provide virtual payment for goods and services free of a central trusted authority. Bitcoin relies on the transmission of digital information, utilizing cryptographic methods to ensure secure, unique transactions. Individuals and businesses transact with the coin electronically on a peerto-peer network utilizing a shared transaction ledger (the Blockchain). It caught wide attention beginning in 2011, and various altcoins a general name for all other cryptocurrencies post-Bitcoin soon appeared It has placed itself as the most widespread and commonly used cryptocurrency with no signs of slowing down (Chan et al., 2017) . Representing over 81% of the total market of cryptocurrencies(coi), Its market capitalization is estimated to be approximately $177.8 Billioncoi accounting for about 90% of the total market capitalization of Virtual Currencies(Houben & Snyers). Bitcoin uses public key cryptography to generate secure addresses for users where each address is a public key, and use of the bitcoins stored in it requires signing with a private key. These address identifiers are used by their owners to hold bitcoin pseudonymously. A typical Bitcoin transaction consists of two sets: a set of source addresses and a set of destination addresses. Coins in the source addresses are collected and then sent in differing amounts to the destination addresses. (Houben & Snyers) While bitcoin address data is publicly available, it is not straightforward to analyze address transaction data since it is not aggregated in one block/place. It is apparent that address2vec is a significant improvement over a baseline approach, although not as accurate as MLSLs we believe further tuning of the model's architecture can yield a more accurate iteration of address2vec, especially making the model end to end differentiable, we currently use separate phases. We also plan to test address2vec on different bitcoin behavior tasks, measuring the similarity of various users and their relationships by measuring their vector distances and predicting market rates of bitcoin through analyzing most recent addresses on the blockchain.
a 2vec model for cryptocurrency transaction graphs The paper proposes to use an autoencoder, networkX, and node2Vec to predict whether a Bitcoin address will become empty after a year, but the results are worse than an existing baseline.