不训练也能精准识别图中异常节点，效率更高。
FreeGAD: A Training-Free yet Effective Approach for Graph Anomaly Detection
- 用无训练的编码器生成异常感知表征
- 通过锚点节点统计偏差计算异常得分
- 无需训练，适合大规模图数据快速部署
图异常检测（GAD）旨在识别图中偏离多数的节点，在社交网络和电子商务等场景中至关重要。尽管基于深度学习的GAD方法已有进展，但其复杂且资源密集的训练过程导致部署成本高、可扩展性差。我们实证发现，深度GAD方法的训练阶段对检测性能的实际贡献远低于预期。受此启发，提出FreeGAD——一种无需训练却有效的新型图异常检测方法。它采用亲和门控残差编码器生成异常感知表征，并选取锚点节点作为伪正常与异常引导，通过锚点引导的统计偏差计算异常得分。大量实验表明，FreeGAD在多个跨领域基准数据集上实现了优越的检测性能、效率与可扩展性，且无需任何训练或迭代优化。
原文摘要 · Abstract (English)
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.
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