Refining Heuristic-Based Bitcoin Address Clustering with Graph Neural Networks
cs.LG
Submitted: 2026-09-01
Updated: 2026-09-01
Comments: 29 pages, 6 figures. Code: https://github.com/hugoschnoering2/btc-graph-hc . Dataset: https://doi.org/10.5281/zenodo.22239038
Code: https://github.com/hugoschnoering2/BTCGraphConstruction
License: http://creativecommons.org/licenses/by-sa/4.0/
The gist: Bitcoin's pseudonymous nature makes it challenging to analyze user-level activity, since a single user may control multiple identifiers (addresses).
Terminology
Abstract
Bitcoin's pseudonymous nature makes it challenging to analyze user-level activity, since a single user may control multiple identifiers (addresses). Existing heuristic-based methods attempt to identify addresses belonging to the same user, but they often produce flat cluster assignments with limited modularity and are prone to errors such as merging different users together. In this work, we propose a method for refining heuristic-obtained clusters by grounding our clustering on contrastive embeddings yielded by graph neural networks. Our contributions are threefold: (i) we release a publicly available dataset of Bitcoin transaction graphs containing a substantial number of clusters; (ii) we propose a methodology for learning address embeddings consistent with heuristics, and back it up with theoretical guiding intuitions; (iii) through hierarchical clustering, we enable a finer analysis of heuristic clusters and provide a quantitative criterion for flagging suspicious merges.
Sources
- The Shape of Money Laundering: Subgraph Representation Learning on the Blockchain with the Elliptic2 Dataset
- Understanding Dimensional Collapse in Contrastive Self-supervised Learning
- Adam: A Method for Stochastic Optimization
- Variational Graph Auto-Encoders
- Semi-Supervised Classification with Graph Convolutional Networks
- Revisiting Graph Neural Networks: All We Have is Low-Pass Filters
- Heuristics for Detecting CoinJoin Transactions on the Bitcoin Blockchain
- Assessing the Efficacy of Heuristic-Based Address Clustering for Bitcoin
- Graph Attention Networks
- Deep Graph Infomax
- Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics
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