研究人员开发了PREF-Gate，一个用于图欺诈检测的新型框架，它解决了将无标签图上下文与标签派生邻域证据融合的挑战。这个可审计的决策框架使用两个专家和一个验证门来管理这些信息来源之间冲突的有效性条件。在Amazon、YelpChi和TFinance数据集上的实验证明了PREF-Gate的有效性，取得了高AUPRC值，并表明只有在得到保留验证的支持下，标签派生的关系证据才有用。 AI
影响 引入了图欺诈检测的新方法，通过更好地处理不完整数据和类别不平衡，可能提高数字生态系统的安全性。
排序理由 该集群包含两篇提交到arXiv的学术论文，详细介绍了图欺诈检测的新方法。
AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →
arXiv:2607.11212v1 Announce Type: new Abstract: Relational fraud detection can exploit both label-free graph context and label-derived neighborhood evidence, but these two information sources obey different validity conditions. In particular, neighborhood risk becomes invalid whe…
arXiv:2607.11107v1 Announce Type: new Abstract: Graph fraud detection plays a pivotal role in safeguarding the security and integrity of modern digital ecosystems. Graph Neural Networks (GNNs) are commonly adopted for graph fraud detection. However, the practical performance of e…
Relational fraud detection can exploit both label-free graph context and label-derived neighborhood evidence, but these two information sources obey different validity conditions. In particular, neighborhood risk becomes invalid when a queried node's own label, or any validation …