Refining Heuristic-Based Bitcoin Address Clustering with Graph Neural Networks

2026-09-01

AI Summary

本文提出使用图神经网络生成对比嵌入来改进基于启发式的比特币地址聚类方法，以更准确地识别同一用户控制的地址，减少错误合并。

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.

Problem

• Bitcoin
• pseudonymity
• address clustering
• heuristics
• transaction graph

Innovation

• Graph Neural Networks
• Contrastive Embeddings
• Hierarchical Clustering
• Bitcoin Address Clustering