发表机构
The Hong Kong Polytechnic University（香港理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
Evi-VN是首个用特征隔离证据链纠正GNN共享难区域的图欺诈检测框架，通过虚拟类节点选择性注入异构证据，可增强各类GNN，在多欺诈任务上验证了优势。
AI 中文摘要
在线平台中存在越来越多模仿合法用户的机器人、欺骗性评论者和诈骗账户，这种伪装模糊了图邻域和行为属性，使得图神经网络（GNN）难以区分伪装良好的欺诈者和合法用户。在各类GNN中，我们观察到它们在共享难区域上存在重叠错误，这表明存在图拓扑结构和标准特征无法捕获的潜在欺诈证据。特定欺诈的GNN可缓解特定图病态，但仍有限利用结构化记录、文本、图像和音频等异构证据；统一多模态融合也可能干扰已被图可靠处理的节点。我们提出Evi-VN，旨在学习并纠正这些共享盲点，而非构建另一个欺诈检测器。据我们所知，Evi-VN是首个使用特征隔离证据链来纠正GNN间共享难区域的图欺诈检测框架，其证据链连接结构化、文本、视觉和声学来源的行为、内容与上下文，助力揭示图邻域可能遗漏的伪装。关键的是，Evi-VN通过虚拟类节点仅将此证据选择性应用于可能的难样本，既保留现有GNN的可靠预测，又保留其输入设计。共享难区域还使Evi-VN即便使用不完美的证据模型，也能增强通用、特定欺诈及未见过的GNN。在机器人、虚假评论、退款证据和电信欺诈任务上的实验验证了这些优势。
英文摘要
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.
Comments17 pages, including supplementary material
