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arXiv Machine Learning By Hugo Schnoering, Roman Bresson, Michalis Vazirgiannis

Refining Heuristic-Based Bitcoin Address Clustering with Graph Neural Networks

Read the original on arXiv Machine Learning →

The paper introduces a method to improve heuristic-based Bitcoin address clustering by using graph neural networks to generate contrastive embeddings. It releases a large Bitcoin transaction graph dataset, presents a learning framework that aligns embeddings with existing heuristics, and applies hierarchical clustering to refine clusters and detect suspicious merges. The approach offers a more modular and theoretically grounded way to analyze user-level activity on the blockchain.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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