Refining Heuristic-Based Bitcoin Address Clustering with Graph Neural Networks

arXiv:2609.01942 · cs.LG · Submitted 2026-09-01 · Read on arXiv

cs.LG

Submitted: 2026-09-01

Updated: 2026-09-01

Code: https://github.com/hugoschnoering2/BTCGraphConstruction

Terminology

• Clustering — 92× in this paper · explained in 2 episodes
• Embeddings — 54× in this paper · explained in 2 episodes
• Address Clustering — 14× in this paper · explained in 1 episode
• GNNs — 12× in this paper · explained in 2 episodes
• Consistency — 6× in this paper · explained in 3 episodes
• Spectral clustering — 6× in this paper · explained in 2 episodes
• Representation Learning — 5× in this paper · explained in 2 episodes
• Contrastive Learning — 4× in this paper · explained in 5 episodes
• Graph Auto-Encoder — 4× in this paper
• Accuracy — 3× in this paper · explained in 1 episode
• Adam — 3× in this paper · explained in 2 episodes
• Graph Construction — 3× in this paper · explained in 3 episodes
• Modularity — 3× in this paper · explained in 9 episodes
• Trace — 3× in this paper · explained in 2 episodes
• Adjacency Matrix — 2× in this paper · explained in 1 episode
• Adjusted Rand Index — 2× in this paper
• Deep Graph Infomax — 2× in this paper
• Generalization — 2× in this paper · explained in 13 episodes
• Hybrid Approach — 2× in this paper · explained in 4 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Ablation study — 1× in this paper · explained in 1 episode
• Activation Function — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Adaptive Graph — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Core — 1× in this paper · explained in 2 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• F1 Score — 1× in this paper · explained in 3 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Feature Selection — 1× in this paper · explained in 4 episodes
• Graph Neural Networks (GNNs) — 1× in this paper · explained in 7 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Information Leakage — 1× in this paper · explained in 2 episodes
• International Conference on Machine Learning and Applications — 1× in this paper
• Interpretability — 1× in this paper · explained in 15 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Self-Supervised Learning — 1× in this paper · explained in 7 episodes
• Semantic Similarity — 1× in this paper · explained in 2 episodes

Sources

• The Shape of Money Laundering: Subgraph Representation Learning on the Blockchain with the Elliptic2 Dataset
• Understanding Dimensional Collapse in Contrastive Self-supervised Learning
• Adam: A Method for Stochastic Optimization
• Variational Graph Auto-Encoders
• Semi-Supervised Classification with Graph Convolutional Networks
• Revisiting Graph Neural Networks: All We Have is Low-Pass Filters
• Heuristics for Detecting CoinJoin Transactions on the Bitcoin Blockchain
• Assessing the Efficacy of Heuristic-Based Address Clustering for Bitcoin
• Graph Attention Networks
• Deep Graph Infomax
• Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics

Related papers

• Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
• AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
• Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
• Convergence issues in Relational Concept Analysis based on AOC-posets
• Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks