Attribute Propagation Enhanced Community Detection Model for Bitcoin De-anonymizing

Jiming Wang, Xueshuo Xie, Yaozheng Fang, Ye Lu, Tao Li, Guiling Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


Bitcoin is a kind of decentralized cryptocurrency on a peer-to-peer network. Anonymity makes Bitcoin widely used in online payment but it is a disadvantage for regulatory purposes. We aim to de-anonymize Bitcoin to assist regulation. Many previous studies have used heuristic clustering or machine learning to analyze historical transactions and identify user behaviors. However, the accuracy of user identification is not ideal. Heuristic clustering only uses the topological structure of the transaction graph and ignores many transaction information, and supervised machine learning methods are limited by the size of labeled datasets. To identify user behaviors, we propose a community detection model based on attribute propagation, combining the topological structure of the transaction graph and additional transaction information. We first parse the transaction data of public ledger and construct a bipartite graph to describe correlations between addresses and transactions. We also extract address attributes from historical transactions to construct an attributed graph with the previous bipartite graph. Then, we design an adaptive weighted attribute propagation algorithm named AWAP running on the attributed graph to classify bitcoin addresses, and further identify user behaviors. Extensive experiments highlight that the proposed detection model based on AWAP achieves 5% higher accuracy on average compared to state-of-the-art address classification methods in Bitcoin. AWAP also achieves 25% higher F-score on average compared to previous community detection algorithms on two datasets.

Original languageEnglish (US)
Title of host publicationMachine Learning for Cyber Security - Third International Conference, ML4CS 2020, Proceedings
EditorsXiaofeng Chen, Hongyang Yan, Qiben Yan, Xiangliang Zhang
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages16
ISBN (Print)9783030622220
StatePublished - 2020
Event3rd International Conference on Machine Learning for Cyber Security, ML4CS 2020 - Guangzhou, China
Duration: Oct 8 2020Oct 10 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12486 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference3rd International Conference on Machine Learning for Cyber Security, ML4CS 2020

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


  • Attribute propagation
  • Bitcoin anonymity
  • Community detection


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