重新思考图异常检测中的对比学习：一种干净视图视角
Rethinking Contrastive Learning in Graph Anomaly Detection: A Clean-View Perspective
AI总结：
针对图异常检测中对比学习受干扰边影响的问题，提出干净视图增强框架CVGAD，含多尺度异常感知和渐进式净化模块，在五个基准数据集上验证了有效性。
摘要：
图异常检测旨在识别基于图的数据中的异常模式，在网络安全和金融欺诈检测等领域具有广泛的应用。现有方法通常依赖对比学习，假设节点与其局部子图之间的相似度较低表示异常。然而，这些方法忽视了一个关键限制：干扰边的存在使这一假设失效，因为它引入了破坏性噪声，损害了对比学习过程。因此，这一限制削弱了有效学习正常模式有意义表示的能力，导致检测性能次优。为解决此问题，我们提出了一种干净视图增强的图异常检测框架（CVGAD），该框架包含一个多尺度异常感知模块，用于识别对比学习过程中的关键干扰源。此外，为减轻单步边移除过程带来的偏差，我们引入了一种新颖的渐进式净化模块。该模块通过迭代识别和移除干扰边来逐步精化图，从而提升模型性能。在五个基准数据集上的大量实验验证了我们方法的有效性。
英文摘要：
Graph anomaly detection aims to identify unusual patterns in graph-based data, with wide applications in fields such as web security and financial fraud detection. Existing methods typically rely on contrastive learning, assuming that a lower similarity between a node and its local subgraph indicates abnormality. However, these approaches overlook a crucial limitation: the presence of interfering edges invalidates this assumption, since it introduces disruptive noise that compromises the contrastive learning process. Consequently, this limitation impairs the ability to effectively learn meaningful representations of normal patterns, leading to suboptimal detection performance. To address this issue, we propose a Clean-View Enhanced Graph Anomaly Detection framework (CVGAD), which includes a multi-scale anomaly awareness module to identify key sources of interference in the contrastive learning process. Moreover, to mitigate bias from the one-step edge removal process, we introduce a novel progressive purification module. This module incrementally refines the graph by iteratively identifying and removing interfering edges, thereby enhancing model performance. Extensive experiments on five benchmark datasets validate the effectiveness of our approach.
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。
