Address2vec: Generating vector embeddings for blockchain analytics
Authors: Ali Hussein
Samiiha Nalwooga
Venue:  (2020)
Keywords: crypto-currency
bitcoin
blockchain
2vec

Abstract: 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.

Under review as a conference paper at ICLR 2020
ADDRESS2VEC: GENERATING VECTOR EMBEDDINGS FOR BLOCKCHAIN ANALYTICS
Anonymous authors Paper under double-blind review
ABSTRACT
Bitcoin is 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 its original struc- ture does not allow for direct analysis of address transactions. Existing analysis methods of the Bitcoin blockchain can be complicated, computationally expen- sive or inaccurate. We propose a computationally efficient model to analyze bit- coin 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.
INTRODUCTION
Bikkoin(Nakaotto) is 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 peer- to-peer network utilizing 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 slow- ing downgChan et al. 2017). Representing over 81 % of the total market of cryptocurrencies(co)) 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 a public key, and use of the bitcoins stored in it requires signing with private key. These address identifiers are used by their owners to hold bitcoin pseudonymously. A typical Bitcoin transaction consists of two sets: set of source addresses and a set of destination addresses. Coins the source addresses are collected and then sent in differing amounts to the destination addresses. (Houben & Snyers)
While bitcoin address data publicly available, not straightforward to analyze address transac- tion data since it is not aggregated in one block/place.
2 CURRENT METHODS
We are comparing our work to multi level sequence learners (MLSas)A et al. 2016) based on the use of multiple levels of HHoMeeeocreiie & Schmidhuber) 1997) to generate a tree of depth D defined by 0 < d < D for each node where each layer d represents the dth neighbourhood of the node. The maximum depth of the tree D is arbitrary and picked to suit accuracy and resources. Multi-level Sequence Leamnrr are class of Long Short Term Memory Networks optimized for generating sparse embeddings of graphs LSTMs scale with depth as for each child node there is at least learner. However, develop address2vec a computationally cheaper and comparable ap- proach extended from previous embedding generation models(Grover & Leskovec, MMioooy al. 2013).

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Under review as a conference paper at ICLR 2020
3 METHODOLOGY
We extract transaction data for each block from a range of selected blocks, we developed a python scrip Tripp[tha that connects to boockchain.infooblll and queries for block data. We then use autoencoder that compresses the features of a consaction(Time, Block Height, Size, Input address count, out- put address count, input value, output value, number of outputs not part of change transaction, transaction fee) of a transaction into a single number. For each block we extract transactions and construct transaction graph G (A) using networkX
et al. 2008) where A is the set of addresses a on the blockchhain where and T is the set of transactions on the blockchain. Where each transaction fa,e, == (ag, Oy, Whee) has a sending address ag and receiving address o,, and the autoencoder compressed features Whte- We use the same weight Whte for all input output pairs in single transaction. We then proceed to use the node2vec algorithm(Leesovee
Sosiē 2 & Leskovec, 2016)) to generate node embeddings for the given graph. We then use a small densely connected network to predict behavior of addresses. We predict whether or not a bitcoin address will be empty after year and compare our results to MLSLs and weighted coin toss baseline.
4 RESULTS
We are able to generate comparable results to MLSLs at a cheaper computational cost. Refer to Table below.
Table 1: Node Classification Prediction Results
Avg. Recall
F-1 (Spent)
F-1 (Hoard)
Baseline 1-MLSL
0.50
0.79
0.20
0.75
0.82
0.62
2-MLSL
0.78
0.85
0.64
3-MLSL
0.77
0.84
0.63
A2V (Dense FNN)
0.71
0.75
0,45
CONCLUSION
It is apparent that address2vec is 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, separate phases. We also plan to test address2vec on different bitcoin behavior tasks, measuring the and their relationships by measuring their vector distances and predicting
currently use
similarity of various market rates of bitcoin through analyzing most recent addresses on the blockchain.
REFERENCES
blockchain.info api. URl.poocccaain info
coinmarketcup.com. URL https:/t:://c http://oooommrrktccc......... com
Rakshit Agrawal, Luca de Alfaro, and Vassilis Polychronopoulos. Learning From Graph Neighborhoods Using LSTMs. arXiv:1611.00 06882 [cs, stat], November 2016. URL http:://aaa:as http://www....rr//////lll... 06882
arXiv: 1611.6688.
Stephen Chan, Jeffrey Chu, Saralees Nadarajah, and Jocrg Osterrieder. A Statistical Analysis of Cryp- tocurrencies. Journal of Risk and Financial Management, 10(2):12, May 2017. ISSN 1911-8074. doi:
10.3399/jrfm11002000 URL http://wwwwmppp.....om com/ 1 911 8074
www.
Aditya Grover and Jure Leskovec, node2vec: Scalable Feature Learning for Networks. arXiv: [es, statj, July 2016. URL hRtlhht:://aaxx. org/ abs/1607 00653
arXiv: 1607.00653.
Aric A. Hagberg, Daniel A. Schult, and Pieter J. Swart. Exploring network structure, dynamics, and function using networkx. In Gaël Varoquaux, Travis Vaught, and Jarrod Millman (cds.), Proceedings the 7th Python in Science Conference, pp. 11 15, Pasadena, CA USA, 2008
Code available gituuh.oom/mcaneclyy
2

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Under review as a conference paper at ICLR 2020
Sepp Hochreiter and Jürgen Schmidhuber. Long short-term memory. Neural Comput. 98)):1335-7880,
November 1997. ISSN 0899-7667. doi: 10.1162/neco. 10.11622he0.119979..... 1993.9.8.1735. URL http://dx.doi.oom
10.1162/necc. 1997 9.8...98.... 735
Dr Robby Houben and Alexander Snyers. Cryptocurrencies blockchain. pp. 103.
Jure Leskovec and Rok Sosiè. Snap: general-purpose network analysis and graph-mining library. ACM Transactions on Intelligent Systems and Technology (TIST), 8(1):1, 2016.
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffree Dean. Efficient Estimation of Word Representations Vector Space. 1rXiv:1301.3781 [cs]. January 2013. URL
abs abr3g//.b///30.././/.//00 3781
arXiv: 1301.3781.
Satoshi Nakamoto, Bitcoin: Peer-to-Peer Electronic Cash System. pp.
A
APPENDIX
You may include other additional sections here.


Reviews:
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It uses autoencoder to extract the feature of the transaction and construct a transaction graph. Then the node2vec algorithm is used to generate node embeddings for the given graph. The task is to predict the behavior of addresses.  The experiments are conducted against Multi Level Sequence Learners (MLSLs), one of the best performing models on bitcoin address data.\n\nPros: \nThis work studies an interesting and challenging problem.\n\nCons\n1. This is an unfinished work. The proposed method lack of detail description.\n2. The performance is much lower than the MLSLs baseline methods. ", "extra_scores": {"experience_assessment": "I have published in this field for several years.", "review_assessment:_thoroughness_in_paper_reading": "I read the paper thoroughly.", "review_assessment:_checking_correctness_of_experiments": "I carefully checked the experiments.", "review_assessment:_checking_correctness_of_derivations_and_theory": "I carefully checked the derivations and theory."}, "extra_text": {}}, {"review_id": "H1ey-GxEFH", "reviewer": "AnonReviewer2", "summary": "", "questions": "", "limitations": "", "rating": 1, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "In this paper, the authors propose a new method for generating vector embeddings. The studied problem is important and the topic is related to area of Bitcoin. \n\nEmpirical studies on some dataset (no description about the dataset) show some results on some evaluation metrics (no clear description about the metrics). For the methods 1-MLSL, 2-MLSL and 3-MLSL, it is seems that they are better than the proposed one (i.e., A2V) on some metrics, which is then inconsistent with the claims in the paper.\n\nMy major concern is that the paper is a bit too short and is lack of some necessary information, for example:\n\n1 The authors are encouraged to provide sufficient background introduction, so that the reader can have a big picture of the problem and area.\n\n2 The authors are encouraged to provide a detailed discussion and justification about the motivation, as well as the challenges and intuitions about the proposed method in the Introduction Section.\n\n3 The authors are encouraged to show a detailed derivation about the technical details, in particular its difference compared with the major baseline, i.e., MLSL.\n\n4 The authors are encouraged to follow the typical writing about the experiments in a paper, e.g., description about the datasets, evaluation metrics, baseline methods, parameter configurations and results analysis, etc. \n\nBased on the above comments, I have to make a reject recommendation.", "extra_scores": {"experience_assessment": "I do not know much about this area.", "review_assessment:_checking_correctness_of_derivations_and_theory": "N/A", "review_assessment:_checking_correctness_of_experiments": "N/A", "review_assessment:_thoroughness_in_paper_reading": "I read the paper at least twice and used my best judgement in assessing the paper."}, "extra_text": {}}, {"review_id": "BJgjf53jOH", "reviewer": "AnonReviewer1", "summary": "", "questions": "", "limitations": "", "rating": 1, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "The paper proposes to use an autoencoder, networkX, and node2Vec in succession to convert a Bitcoin transaction to a vector. This is then used to predict whether a Bitcoin address will become empty after a year. The results are better than flipping a coin, but worse than an existing baseline.\n\nGiven the apparent lack of any technical contribution to machine learning theory or practice, the inconclusive empirical results, and the generally unpolished writing (e.g., long run-on sentence in the conclusion, vague problem definition), I do not believe this paper is suitable for publication.", "extra_scores": {"experience_assessment": "I do not know much about this area.", "review_assessment:_checking_correctness_of_derivations_and_theory": "I carefully checked the derivations and theory.", "review_assessment:_checking_correctness_of_experiments": "I carefully checked the experiments.", "review_assessment:_thoroughness_in_paper_reading": "I read the paper thoroughly."}, "extra_text": {}}]

Author rebuttal:

Meta-review: The paper propose to analyze bitcoin addresses using graph embeddings. The reviewers found that the paper was too incomplete for publication. Important information such as a description of datasets and metrics was omitted.
Decision: Reject