# Refining Heuristic-Based Bitcoin Address Clustering with Graph Neural Networks

arXiv: https://arxiv.org/abs/2609.01942 · HTML: https://mlsanity.com/paper/2609.01942/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 31 of its 57 references that are in the map.
- First posted on arXiv 1 Sep 2026.

## Builds on (most cited first, 30 of 31)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Variational Graph Auto-Encoders](https://mlsanity.com/paper/1611.07308/) (2016)
- [Deep Graph Infomax](https://mlsanity.com/paper/1809.10341/) (2018)
- [Hierarchical Graph Representation Learning with Differentiable Pooling](https://mlsanity.com/paper/1806.08804/) (2018)
- [Self-Attention Graph Pooling](https://mlsanity.com/paper/1904.08082/) (2019)
- [From Louvain to Leiden: guaranteeing well-connected communities](https://mlsanity.com/paper/1810.08473/) (2018)
- [Understanding Dimensional Collapse in Contrastive Self-supervised Learning](https://mlsanity.com/paper/2110.09348/) (2021)
- [Provable Guarantees for Self-Supervised Deep Learning with Spectral Contrastive Loss](https://mlsanity.com/paper/2106.04156/) (2021)
- [Revisiting Graph Neural Networks: All We Have is Low-Pass Filters](https://mlsanity.com/paper/1905.09550/) (2019)
- [Position-aware Graph Neural Networks](https://mlsanity.com/paper/1906.04817/) (2019)
- [Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics](https://mlsanity.com/paper/1908.02591/) (2019)
- [Contrastive Learning Is Spectral Clustering On Similarity Graph](https://mlsanity.com/paper/2303.15103/) (2023)
- [Subgraph Pooling: Tackling Negative Transfer on Graphs](https://mlsanity.com/paper/2402.08907/) (2024)
- [You Can Have Better Graph Neural Networks by Not Training Weights at All: Finding Untrained GNNs Tickets](https://mlsanity.com/paper/2211.15335/) (2022)
- [Scalable Hierarchical Agglomerative Clustering](https://mlsanity.com/paper/2010.11821/) (2020)
- [The Unreasonable Effectiveness of Address Clustering](https://mlsanity.com/paper/1605.06369/) (2016)
- [An Evaluation of Bitcoin Address Classification based on Transaction History Summarization](https://mlsanity.com/paper/1903.07994/) (2019)
- [The Shape of Money Laundering: Subgraph Representation Learning on the Blockchain with the Elliptic2 Dataset](https://mlsanity.com/paper/2404.19109/) (2024)
- [TeraHAC: Hierarchical Agglomerative Clustering of Trillion-Edge Graphs](https://mlsanity.com/paper/2308.03578/) (2023)
- [Resurrecting Address Clustering in Bitcoin](https://mlsanity.com/paper/2107.05749/) (2021)
- [Heuristics for Detecting CoinJoin Transactions on the Bitcoin Blockchain](https://mlsanity.com/paper/2311.12491/) (2023)
- [Bitcoin Research with a Transaction Graph Dataset](https://mlsanity.com/paper/2411.10325/) (2024)
- [Connecting Spectral Clustering to Maximum Margins and Level Sets](https://mlsanity.com/paper/1812.06397/) (2018)
- [Demystifying Bitcoin Address Behavior via Graph Neural Networks](https://mlsanity.com/paper/2211.14582/) (2022)
- [Assessing the Efficacy of Heuristic-Based Address Clustering for Bitcoin](https://mlsanity.com/paper/2403.00523/) (2024)

## Abstract (arXiv)

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.