重新审视图对比学习在异常检测中的应用：结构不平衡视角
Revisiting Graph Contrastive Learning on Anomaly Detection: A Structural Imbalance Perspective
AI总结：
本文针对图对比学习在异常检测中对结构不平衡鲁棒性不足的问题，提出AD-GCL框架，通过邻居剪枝、异常引导邻居补全及视图一致性损失，提升对头部和尾部异常节点的检测能力。
摘要：
图对比学习（GCL）的优越性促使其被应用于异常检测任务，以构建更强大的风险预警系统。然而，现有基于GCL的模型往往过度优先考虑整体检测性能，而忽视了对结构不平衡的鲁棒性，这对于许多遵循幂律度分布的真实世界网络而言可能存在问题。特别是，基于GCL的方法可能无法捕获尾部异常（度数较低的异常节点）。这引发了人们对当前异常检测算法安全性和鲁棒性的担忧，从而阻碍了其在各种现实高风险场景中的适用性。据我们所知，关于图异常检测对结构不平衡鲁棒性的研究很少受到关注。为解决上述问题，本文提出了一种新颖的基于GCL的框架，名为AD-GCL。该框架设计了邻居剪枝策略，以过滤头部节点的噪声边，并通过从头部节点对齐到伪造尾部节点来促进对真实尾部节点的检测。此外，AD-GCL通过异常引导的邻居补全，主动探索潜在邻居以扩大尾部节点的感受野。我们进一步引入原始图与增强图的视图内和视图间一致性损失，以增强表示。在多个数据集上对整体、头部和尾部节点的性能评估验证了所提出的AD-GCL在检测头部异常和尾部异常方面的全面优越性。
英文摘要：
The superiority of graph contrastive learning (GCL) has prompted its application to anomaly detection tasks for more powerful risk warning systems. Unfortunately, existing GCL-based models tend to excessively prioritize overall detection performance while neglecting robustness to structural imbalance, which can be problematic for many real-world networks following power-law degree distributions. Particularly, GCL-based methods may fail to capture tail anomalies (abnormal nodes with low degrees). This raises concerns about the security and robustness of current anomaly detection algorithms and therefore hinders their applicability in a variety of realistic high-risk scenarios. To the best of our knowledge, research on the robustness of graph anomaly detection to structural imbalance has received little scrutiny. To address the above issues, this paper presents a novel GCL-based framework named AD-GCL. It devises the neighbor pruning strategy to filter noisy edges for head nodes and facilitate the detection of genuine tail nodes by aligning from head nodes to forged tail nodes. Moreover, AD-GCL actively explores potential neighbors to enlarge the receptive field of tail nodes through anomaly-guided neighbor completion. We further introduce intra- and inter-view consistency loss of the original and augmentation graph for enhanced representation. The performance evaluation of the whole, head, and tail nodes on multiple datasets validates the comprehensive superiority of the proposed AD-GCL in detecting both head anomalies and tail anomalies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
