Refining Heuristic-Based Bitcoin Address Clustering with Graph Neural Networks
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Computer Science > Machine Learning
Title:Refining Heuristic-Based Bitcoin Address Clustering with Graph Neural Networks
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.
| Comments: | 29 pages, 6 figures. Code: this https URL . Dataset: this https URL |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2609.01942 [cs.LG] |
| (or arXiv:2609.01942v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2609.01942
arXiv-issued DOI via DataCite (pending registration)
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