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Illegal Community Detection in Bitcoin Transaction Networks.

Dany Kamuhanda1,2,3, Mengtian Cui4, Claudio J Tessone1,2

  • 1UZH Blockchain Center, University of Zurich, 8050 Zurich, Switzerland.

Entropy (Basel, Switzerland)
|July 29, 2023
PubMed
Summary
This summary is machine-generated.

Community detection in cryptocurrency networks identifies illicit activity. This study found that 0.06% of Bitcoin communities contain illegal addresses, highlighting the effectiveness of community quality optimization and label propagation methods.

Keywords:
bitcoinblockchaincommunity detectioncryptocurrencytransaction networks

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Area of Science:

  • Cybersecurity
  • Network Analysis
  • Blockchain Technology

Background:

  • Community detection is vital for social network analysis.
  • Cryptocurrency transaction networks present unique challenges for community detection due to pseudonymity and multi-address ownership.
  • Identifying illicit activities within these networks is a significant concern.

Purpose of the Study:

  • To investigate the effectiveness of community detection methods for identifying illegal activities in Bitcoin transaction networks.
  • To address the challenges posed by pseudonymous addresses and multi-address ownership.
  • To determine the most suitable community detection algorithms for this domain.

Main Methods:

  • Analysis of Bitcoin transaction network structures.
  • Collection and utilization of a dataset of known illegal Bitcoin addresses for community labeling.
  • Evaluation of various community detection algorithms, including distance-based, community quality optimization, and label-propagation methods.

Main Results:

  • 0.06% of detected communities contained one or more illegal Bitcoin addresses, based on a dataset of 2,313,344 illegal addresses.
  • Distance-based clustering and network representation learning methods proved unsuitable for Bitcoin transaction networks.
  • Community quality optimization and label-propagation-based methods demonstrated the highest suitability and effectiveness.

Conclusions:

  • Community detection is a viable approach for identifying illicit clusters within cryptocurrency networks.
  • Label-propagation and community quality optimization methods are recommended for analyzing Bitcoin transaction networks.
  • Further research can refine these methods for enhanced detection of financial crime in blockchain ecosystems.