Hugging Face Trending Papers
Refining Heuristic-Based Bitcoin Address Clustering with Graph Neural Networks
Read the original on Hugging Face Trending Papers →The Flow has not summarised this story yet — read it at Hugging Face Trending Papers.
Related stories
arXiv Machine Learning
Sep 3
Refining Heuristic-Based Bitcoin Address Clustering with Graph Neural Networks
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.
arXiv Machine Learning
Jul 31
Compression-Based Behavioral Similarity for Open-World Sybil Discovery on Ethereum
arXiv:2607. 27370v1 Announce Type: new Abstract: Sybil attackers are Blockchain actors that adopt the characteristics of regular users to exploit airdrops or influence governance.
arXiv Machine Learning
Sep 24
Quality over Quantity: Semi-Supervised Detection of Illicit Bitcoin Flows via Feature Engineering
The paper presents a semi‑supervised learning framework for detecting illicit Bitcoin flows in Shared Send Mixers, using a large historical dataset of 163 million transactions. It demonstrates that the success of SSL depends on data quality rather than sheer volume, with high‑fidelity features such as KeyLinker address clustering and Shared Send Untangling complexity metrics achieving an F1 score of 0.84 on unlabeled data. The study also shows that common heuristics like One‑Time Change introduce noise, underscoring the importance of smarter feature engineering in blockchain forensics.
arXiv Machine Learning
Jun 16
Dynamic Link Prediction with Temporally Enhanced Signed Graph Neural Networks
arXiv:2605. 26290v2 Announce Type: replace Abstract: Temporal signed networks (TSNs) model the time evolution of cooperative and adversarial relationships that arise in applications such as social media analysis, trust and reputation systems, and financial transaction networks.
arXiv AI
Jun 26
TransXion: A High-Fidelity Graph Benchmark for Realistic Anti-Money Laundering
arXiv:2604. 17420v2 Announce Type: replace-cross Abstract: Money laundering poses severe risks to global financial systems, driving the widespread adoption of machine learning for transaction monitoring.
Hugging Face Trending Papers
Aug 20
Catching the Rug: Early Prediction of Fraudulent Memecoins on Solana via Machine Learning
The paper presents a method for early detection of fraudulent memecoins (rug pulls) on the Solana blockchain, using a dataset of 6.4 million tokens collected over seven months. It shows that most rug pulls occur within an hour of launch and that classic machine learning models, especially Gradient Boosting (XGBoost), can reliably predict them using only the first five minutes of trading data. Cross‑platform data fusion between PumpFun and Raydium further improves detection by reducing domain shift.