arXiv：cs.LG· Jiran Tao, Yifan Wu, Binyan Jiang·· 4 小时前
Evi-VN：面向 GNN 欺诈检测的硬区域引导虚拟节点证据注入
Evi-VN: Hard Region Guided Virtual Node Evidence Injection for GNN-Based Fraud Detection
AI 导读
Evi-VN 是一个面向 GNN 欺诈检测的框架，首次用特征隔离的证据链修正多个 GNN 共享的硬区域错误。其证据链跨结构化、文本、视觉与音频来源连接行为、内容与上下文，并通过虚拟类节点仅对可能的困难样本选择性注入证据，保留原有 GNN 的可靠预测与输入设计。在 bot、虚假评论、退款证据与电信欺诈任务上的实验验证了该方法的优势。
正文
Abstract:Online platforms contain growing numbers of bots, deceptive reviewers, and scam accounts that imitate legitimate users. Such camouflage blurs graph neighborhoods and behavioral attributes, making it difficult for graph neural networks (GNNs) to distinguish both well-disguised fraudsters and legitimate users. Across diverse GNNs, we observe overlapping errors on a shared hard region, suggesting the presence of latent fraud evidence that graph topologies and standard features fail to capture. Fraud-specific GNNs can mitigate particular graph pathologies, yet they still make limited use of heterogeneous evidence such as structured records, text, images, and audio; uniform multimodal fusion may also disturb nodes already handled reliably by the graph. We propose Evi-VN to learn and correct these shared blind spots rather than build another fraud detector. To our knowledge, Evi-VN is the first graph fraud detection framework to use feature isolated evidence chains to correct hard regions shared across GNNs. Its evidence chains connect behavior, content, and context across structured, textual, visual, and acoustic sources, helping expose camouflage that graph neighborhoods may miss. Crucially, Evi-VN selectively applies this evidence only to likely hard samples via virtual class nodes, preserving both the reliable predictions and the input design of existing GNNs. Shared hard regions also let Evi-VN enhance generic, fraud-specific, and unseen GNNs even with imperfect evidence models. Experiments across bot, fake-review, refund-evidence, and telecom-fraud tasks validate these advantages.
| Comments: | 17 pages, including supplementary material |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.11665 [cs.LG] |
| (or arXiv:2610.11665v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.11665 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jiran Tao [view email]
[v1]
Thu, 8 Oct 2026 10:39:10 UTC (927 KB)
来源：arXiv：cs.LG · arxiv.org