Semantically Complementary Spectral Views Learning for Graph-Level Anomaly Detection

Graph-level anomaly detection (GLAD) is a critical task in domains like social networks and bioinformatics. However, current unsupervised contrastive learning-based methods rely on semantically homogeneous augmentations, leading to catastrophic informational overlap that trivializes the learning objective and effectively masks anomalous signals in GLAD. To overcome this, we propose Sima (Semantically Complementary Spectral Views), a novel contrastive framework that learns from semantically complementary spectral views rather than redundant homogeneous views. Sima first constructs a pair of spectral views to separately capture global community structure and local heterophilous variations, which together cover the full graph spectrum of anomalous patterns. Inspired by the Multi-View Information Bottleneck principle, we further design a soft subgraph extraction module with Score Reconstruction Attention (SRA) and an anchor-guided condensation mechanism to distill the essential shared information from each view into a compact representation. We then optimize a contrastive objective across these distilled views to enforce cross-view alignment and amplify subtle structural deviations. Extensive experiments on challenging GLAD and graph-level out-of-distribution detection (GLOD) benchmarks demonstrate that Sima achieves substantial and consistent gains in average performance over state-of-the-art baselines.