发表机构
Tongji University; Tencent Inc.（同济大学; 腾讯公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对微信支付十亿级用户信用风险检测需求，提出风险感知重叠子图学习框架，解决工业级GNN拓扑完整性损失问题，实验显示其性能优于现有策略。
AI 中文摘要
信用风险检测，尤其是缓解个人欺诈，对维护数字金融生态系统的稳定性至关重要。在数十亿用户中准确识别信用欺诈，对于最小化金融损失、保障普惠金融服务的可持续性至关重要。由于信用欺诈风险常隐藏在异构用户-风险图中，图神经网络（GNN）通过捕捉复杂依赖关系成为风险挖掘的有效工具。为解决工业级GNN的可扩展性瓶颈，基于子图的分布式训练不可或缺，但现有策略常为负载均衡牺牲拓扑完整性，这对风险检测是灾难性的，因其会无差别切断风险传播必需的长尾证据链。重叠子图可恢复被切断的风险上下文，但不可避免引入冗余和噪声，且忽视不同局部子图间的表示对齐。本文提出面向大规模信用风险检测的风险感知重叠子图学习框架：首先构建基础划分以确保负载均衡；随后执行预算约束采样，选取有信息的长尾节点，在过滤噪声的同时保留关键风险扩散模式；为缓解表示不一致，设计跨子图一致性对齐机制，通过对重叠节点施加对齐约束，将局部表示协调到全局一致的隐空间。在微信支付生产数据集上的大量实验表明，该模型显著优于现有风险检测策略，为工业级图学习提供了可扩展且有效的解决方案。
英文摘要
Credit risk detection, particularly mitigating individual fraud, is crucial for maintaining the stability of digital financial ecosystems. Accurately identifying credit fraud among billions of users is critical for minimizing financial losses and safeguarding the sustainability of inclusive financial services. Given that credit fraud risks are often concealed within heterogeneous user-risk graphs, Graph Neural Networks (GNNs) have emerged as an effective tool for risk mining by capturing complex dependencies. To address the scalability bottleneck of industrial GNNs, distributed training based on subgraphs is indispensable. However, existing strategies often compromise topological integrity for load balancing. This can be catastrophic for risk detection, as it indiscriminately severs the long-tail evidence chains essential for risk propagation. Overlapping subgraphs can restore severed risk contexts but inevitably introduce redundancy and noise, while overlooking the representation alignment across different local subgraphs. In this paper, we propose a risk-aware overlapping subgraph learning framework for large-scale credit risk detection. We first construct base partitions to ensure load balance. Then, we perform budget-constrained sampling that selects informative long-tail nodes, thereby preserving critical risk diffusion patterns while filtering out noise. To mitigate representation inconsistency, we design a cross-subgraph consistency alignment mechanism. By enforcing alignment constraints on the overlapping nodes, we harmonize the local representations into a globally consistent latent space. Extensive experiments on Weixin Pay's production dataset demonstrate that our model significantly outperforms existing strategies for risk detection, offering a scalable and effective solution for industrial graph learning.
Journal refProceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '26), pp. 7679-7690, 2026
