GADMVP: Adaptive Few-Shot Graph-Level Anomaly Detection with Multi-View Structured Prompting

Graph-level anomaly detection plays a critical role in a wide range of applications, such as fraud detection in financial networks and malicious program detection, yet it remains challenging in real-world scenarios where labeled anomalous graphs are extremely scarce and graph structures are highly diverse. Existing few-shot methods often suffer from severe performance degradation due to the effects of class imbalance and structural imbalance, and they struggle to generalize to structurally deviant or rare anomalies. In this paper, we propose an adaptive few-shot graph-level anomaly detection model with multi-view structure-aware prompting (GADMVP). To mitigate structural imbalance, we construct a graph-of-graphs representation that facilitates cross-graph message passing, capturing both intra-graph semantics and inter-graph dependencies. Building upon these priors, we introduce a structure-aware graph prompting mechanism that extracts and adaptively refines anomaly-relevant prompts from a small set of labeled graphs. Extensive experiments on multiple benchmark datasets demonstrate that the proposed method consistently outperforms state-of-the-art few-shot graph anomaly detection baselines, while exhibiting strong robustness under extremely limited labeled data. The code and implementation details are available at https://anonymous.4open.science/r/GLAD-3456543/.