HiLoMix: Robust High- and Low-Frequency Graph Learning Framework for Mixing Address Association
Xiaofan Tu, Tiantian Duan, Shuyi Miao, Hanwen Zhang, Yi Sun
随着混合服务越来越多地被恶意行为者利用进行非法交易,混合地址关联已成为一项重要的研究任务。 已经探索了一系列方法,基于图形的模型因其在交易网络中捕获结构模式的能力而脱颖而出。 然而,这些方法面临两个主要挑战:标签噪声和标签稀缺性,导致性能欠优和有限的推广。 为了解决这些问题,我们提出了HiLoMix,这是一个基于图形的学习框架,专门用于混合地址关联。 首先,我们构建了异质属性混合相互作用图(HAMIG),以丰富拓扑结构。 其次,我们引入了频率感知图对比度学习,从高频率和低频图形视图中捕获互补的结构信号。 第三,我们使用弱监督学习,为嘈杂的标签分配基于信心的权重。 然后,我们使用无监督的对比信号和基于置信的监督共同训练高通和低通GNN,以学习强大的节点表示。 最后,我们采用堆叠框架来融合来自多个异构模型的预测,进一步提高了概括性和鲁棒性。 实验结果表明,HiLoMix在混合地址关联方面优于现有方法。
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 HiLoMi...