Adaptive Graph Filtering Neural Network for Graph Anomaly Detection

Zhizhe Liu, Shuai Zheng, Yeyu Yan, Zhenfeng Zhu, Yao Zhao

Abstract: Graph anomaly detection (GAD) plays an important role in improving public safety and product quality and has attracted a great deal of interest in recent years. Although a wide range of progress has been achieved recently, the following challenges still remain: (1) abnormal nodes mixed in the normal node subgraph and (2) global-consistency filtering to different features. To overcome these challenges, we propose AGFNN, a novel adaptive graph filtering neural network designed to handle diverse mixed local patterns and feature variations, thereby improving model fitting from both the node and feature perspectives. Specifically, to enhance the discriminative capacity of the node representation, channel-wise feature adaptive filtering is proposed to learn a specific filter for each feature in a progressive way, which first performs multi-frequency filtering and then adaptively captures the importance of different frequency components for each feature. Meanwhile, to better fit the complex local subgraphs, the node's preference for multi-frequency information can be self-adjusted by learning node-aware bias, which is also equal to learning a specific filter for each node. Extensive experiments on real-world graph datasets demonstrate that AGFNN outperforms the state-of-the-art methods.

External IDs:dblp:journals/tnse/LiuZYZZ26