Addressing Graph Anomaly Detection via Causal Edge Separation and Spectrum

• URL: http://arxiv.org/abs/2508.14684v1
• Date: Wed, 20 Aug 2025 12:59:22 GMT
• Title: Addressing Graph Anomaly Detection via Causal Edge Separation and Spectrum
• Authors: Zengyi Wo, Wenjun Wang, Minglai Shao, Chang Liu, Yumeng Wang, Yueheng Sun,
• Abstract summary: In the real world, anomalous entities often add more legitimate connections while hiding direct links with other anomalous entities.<n>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.<n>We propose a spectral neural network CES2-GAD based on causal edge separation for anomaly detection on heterophilic graphs.
• Score: 6.722106415172189
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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