中文标题#
通过因果边分离和谱方法解决图异常检测
英文标题#
Addressing Graph Anomaly Detection via Causal Edge Separation and Spectrum
中文摘要#
在现实世界中，异常实体通常会添加更多合法连接，同时隐藏与其他异常实体的直接链接，导致异常网络中出现异质结构，而大多数基于图神经网络（GNN）的技术无法解决这一问题。 已在空间域提出了一些方法来解决这个问题。 然而，这些方法忽略了节点结构编码、节点特征及其上下文环境之间的复杂关系，并依赖于原则性指导，因此在解决谱域异质性问题方面的研究仍然有限。 本研究分析了不同异质程度节点的谱分布，并发现异常节点的异质性会导致谱能量从低频向高频转移。 为了解决上述挑战，我们提出了一种基于因果边分离的谱神经网络 CES2-GAD，用于异质图上的异常检测。 首先，CES2-GAD 将原始图通过因果干预分为同质边和异质边。 随后，使用各种混合谱滤波器从分段图中捕获信号。 最后，将多个信号的表示进行拼接并输入分类器以预测异常。 与真实数据集的大量实验已证明了我们提出的方法的有效性。
英文摘要#
In the real world, anomalous entities often add more legitimate connections while hiding direct links with other anomalous entities, leading to heterophilic structures in anomalous networks that most GNN-based techniques fail to address. Several works have been proposed to tackle this issue in the spatial domain. However, these methods overlook the complex relationships between node structure encoding, node features, and their contextual environment and rely on principled guidance, research on solving spectral domain heterophilic problems remains limited. This study analyzes the spectral distribution of nodes with different heterophilic degrees and discovers that the heterophily of anomalous nodes causes the spectral energy to shift from low to high frequencies. To address the above challenges, we propose a spectral neural network CES2-GAD based on causal edge separation for anomaly detection on heterophilic graphs. Firstly, CES2-GAD will separate the original graph into homophilic and heterophilic edges using causal interventions. Subsequently, various hybrid-spectrum filters are used to capture signals from the segmented graphs. Finally, representations from multiple signals are concatenated and input into a classifier to predict anomalies. Extensive experiments with real-world datasets have proven the effectiveness of the method we proposed.
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