{"ID":2843092,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.07759","arxiv_id":"2511.07759","title":"HiLoMix: Robust High- and Low-Frequency Graph Learning Framework for Mixing Address Association","abstract":"As mixing services are increasingly being exploited by malicious actors for illicit transactions, mixing address association has emerged as a critical research task. A range of approaches have been explored, with graph-based models standing out for their ability to capture structural patterns in transaction networks. However, these approaches face two main challenges: label noise and label scarcity, leading to suboptimal performance and limited generalization. To address these, we propose HiLoMix, a graph-based learning framework specifically designed for mixing address association. First, we construct the Heterogeneous Attributed Mixing Interaction Graph (HAMIG) to enrich the topological structure. Second, we introduce frequency-aware graph contrastive learning that captures complementary structural signals from high- and low-frequency graph views. Third, we employ weak supervised learning that assigns confidence-based weighting to noisy labels. Then, we jointly train high-pass and low-pass GNNs using both unsupervised contrastive signals and confidence-based supervision to learn robust node representations. Finally, we adopt a stacking framework to fuse predictions from multiple heterogeneous models, further improving generalization and robustness. Experimental results demonstrate that HiLoMix outperforms existing methods in mixing address association.","short_abstract":"As mixing services are increasingly being exploited by malicious actors for illicit transactions, mixing address association has emerged as a critical research task. A range of approaches have been explored, with graph-based models standing out for their ability to capture structural patterns in transaction networks. H...","url_abs":"https://arxiv.org/abs/2511.07759","url_pdf":"https://arxiv.org/pdf/2511.07759v2","authors":"[\"Xiaofan Tu\",\"Tiantian Duan\",\"Shuyi Miao\",\"Hanwen Zhang\",\"Yi Sun\"]","published":"2025-11-11T02:19:00Z","proceeding":"cs.SI","tasks":"[\"cs.SI\",\"cs.CR\"]","methods":"[\"Graph Neural Network\"]","has_code":false}