Balanced Anomaly-guided Ego-graph Diffusion Model for Inductive Graph Anomaly Detection
It solves the problem of detecting rare anomalies in dynamic graphs for applications like fraud detection, but it is incremental as it builds on existing GNN methods.
The paper tackles the challenges of inductive graph anomaly detection by addressing transductive learning limitations and class imbalance, proposing a data-centric framework with ego-graph diffusion and curriculum augmentation that improves detection and generalization across five datasets.
Graph anomaly detection (GAD) is crucial in applications like fraud detection and cybersecurity. Despite recent advancements using graph neural networks (GNNs), two major challenges persist. At the model level, most methods adopt a transductive learning paradigm, which assumes static graph structures, making them unsuitable for dynamic, evolving networks. At the data level, the extreme class imbalance, where anomalous nodes are rare, leads to biased models that fail to generalize to unseen anomalies. These challenges are interdependent: static transductive frameworks limit effective data augmentation, while imbalance exacerbates model distortion in inductive learning settings. To address these challenges, we propose a novel data-centric framework that integrates dynamic graph modeling with balanced anomaly synthesis. Our framework features: (1) a discrete ego-graph diffusion model, which captures the local topology of anomalies to generate ego-graphs aligned with anomalous structural distribution, and (2) a curriculum anomaly augmentation mechanism, which dynamically adjusts synthetic data generation during training, focusing on underrepresented anomaly patterns to improve detection and generalization. Experiments on five datasets demonstrate that the effectiveness of our framework.