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Graph Neural Networks Refine Bitcoin Address Clustering

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method to refine Bitcoin address clustering using graph neural networks (GNNs). This approach aims to improve the accuracy of identifying addresses belonging to the same user, addressing limitations in existing heuristic-based methods that can lead to errors. The work includes the release of a new dataset of Bitcoin transaction graphs, a methodology for learning address embeddings aligned with heuristics, and a hierarchical clustering technique for more granular analysis and detection of suspicious merges. AI

IMPACT Enhances blockchain analysis capabilities by improving the accuracy of identifying user-level activity on pseudonymous networks.

RANK_REASON The cluster contains an academic paper detailing a new methodology and dataset for refining Bitcoin address clustering using graph neural networks. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Graph Neural Networks Refine Bitcoin Address Clustering

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The cluster contains an academic paper detailing a new methodology and dataset for refining Bitcoin address clustering using graph neural networks. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Hugo Schnoering, Roman Bresson, Michalis Vazirgiannis ·

    Refining Heuristic-Based Bitcoin Address Clustering with Graph Neural Networks

    arXiv:2609.01942v1 Announce Type: new 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 sam…

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