  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.
