FreeGAD：一种免训练且有效的图异常检测方法
FreeGAD: A Training-Free yet Effective Approach for Graph Anomaly Detection
AI总结：
本文提出免训练的图异常检测方法FreeGAD，通过亲和力门控残差编码器和锚节点引导的统计偏差计算异常分数，在多个基准数据集上以更低成本实现了优越的检测性能、效率和可扩展性。
摘要：
图异常检测（GAD）旨在识别图中偏离大多数节点的异常节点，在社交网络和电子商务等应用中发挥着关键作用。尽管当前基于深度学习的GAD取得了进展，但现有方法由于复杂且资源密集的训练过程，往往面临部署成本高和可扩展性差的问题。令人惊讶的是，我们的实证发现表明，深度GAD方法的训练阶段通常被认为至关重要，但其对异常检测性能的贡献实际上可能低于预期。受此启发，我们提出了FreeGAD，一种新颖的免训练且有效的GAD方法。具体而言，它利用亲和力门控残差编码器生成异常感知表示。同时，FreeGAD将锚节点识别为伪正常和异常引导，随后通过锚引导的统计偏差计算异常分数。大量实验表明，FreeGAD在来自不同领域的多个基准数据集上，无需任何训练或迭代优化，即可实现优越的异常检测性能、效率和可扩展性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
