FreeGAD: A Training-Free yet Effective Approach for Graph Anomaly Detection
This addresses the issue of resource-intensive training for graph anomaly detection in applications like social networks and e-commerce, offering a more scalable solution.
The paper tackles the problem of high deployment costs and poor scalability in graph anomaly detection by proposing FreeGAD, a training-free method that achieves superior performance, efficiency, and scalability on multiple benchmark datasets without any training.
Graph Anomaly Detection (GAD) aims to identify nodes that deviate from the majority within a graph, playing a crucial role in applications such as social networks and e-commerce. Despite the current advancements in deep learning-based GAD, existing approaches often suffer from high deployment costs and poor scalability due to their complex and resource-intensive training processes. Surprisingly, our empirical findings suggest that the training phase of deep GAD methods, commonly perceived as crucial, may actually contribute less to anomaly detection performance than expected. Inspired by this, we propose FreeGAD, a novel training-free yet effective GAD method. Specifically, it leverages an affinity-gated residual encoder to generate anomaly-aware representations. Meanwhile, FreeGAD identifies anchor nodes as pseudo-normal and anomalous guides, followed by calculating anomaly scores through anchor-guided statistical deviations. Extensive experiments demonstrate that FreeGAD achieves superior anomaly detection performance, efficiency, and scalability on multiple benchmark datasets from diverse domains, without any training or iterative optimization.