GLADformer: A Mixed Perspective for Graph-level Anomaly Detection
This work addresses graph-level anomaly detection for applications in domains like social networks or bioinformatics, representing an incremental improvement by combining existing techniques with spectral enhancements.
The paper tackled the problem of graph-level anomaly detection by proposing GLADformer, a hybrid model that integrates global and spectral features to overcome limitations in receptive fields and spectral exploration, achieving state-of-the-art performance on ten real-world datasets.
Graph-Level Anomaly Detection (GLAD) aims to distinguish anomalous graphs within a graph dataset. However, current methods are constrained by their receptive fields, struggling to learn global features within the graphs. Moreover, most contemporary methods are based on spatial domain and lack exploration of spectral characteristics. In this paper, we propose a multi-perspective hybrid graph-level anomaly detector namely GLADformer, consisting of two key modules. Specifically, we first design a Graph Transformer module with global spectrum enhancement, which ensures balanced and resilient parameter distributions by fusing global features and spectral distribution characteristics. Furthermore, to uncover local anomalous attributes, we customize a band-pass spectral GNN message passing module that further enhances the model's generalization capability. Through comprehensive experiments on ten real-world datasets from multiple domains, we validate the effectiveness and robustness of GLADformer. This demonstrates that GLADformer outperforms current state-of-the-art models in graph-level anomaly detection, particularly in effectively capturing global anomaly representations and spectral characteristics.