用图神经网络优化比特币地址聚类，提升用户识别准确性。
Refining Heuristic-Based Bitcoin Address Clustering with Graph Neural Networks
- 基于对比学习的图神经网络生成地址嵌入，融合启发式规则。
- 提出层级聚类方法，可量化标记可疑的错误合并。
- 公开大规模比特币交易图数据集，支持复现与研究。
比特币的伪匿名性使得分析用户行为困难，因为单个用户可能控制多个地址。现有基于启发式的方法虽尝试识别同一用户的地址，但常导致扁平化的聚类结果，模块化程度低且易将不同用户错误合并。本文提出一种方法，通过图神经网络生成的对比嵌入来优化启发式聚类结果。贡献包括：(i) 发布包含大量聚类的公开比特币交易图数据集；(ii) 提出与启发式一致的地址嵌入学习方法，并提供理论指导；(iii) 采用层次聚类实现更精细的分析，并提供量化标准以标记可疑合并。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。