Graph Anomaly Detection (GAD) is a technique used to identify abnormal nodes within graphs, finding applications in network security, fraud detection, social media spam detection, and various other domains. A common method for GAD is Graph Auto-Encoders (GAEs), which encode graph data into node representations and identify anomalies by assessing the reconstruction quality of the graphs based on these representations. However, existing GAE models are primarily optimized for direct link reconstruction, resulting in nodes connected in the graph being clustered in the latent space. As a result, they excel at detecting cluster-type structural anomalies but struggle with more complex structural anomalies that do not conform to clusters. To address this limitation, we propose a novel solution called GAD-NR, a new variant of GAE that incorporates neighborhood reconstruction for graph anomaly detection. GAD-NR aims to reconstruct the entire neighborhood of a node, encompassing the local structure, self-attributes, and neighbor attributes, based on the corresponding node representation. By comparing the neighborhood reconstruction loss between anomalous nodes and normal nodes, GAD-NR can effectively detect any anomalies. Extensive experimentation conducted on six real-world datasets validates the effectiveness of GAD-NR, showcasing significant improvements (by up to 30% in AUC) over state-of-the-art competitors. The source code for GAD-NR is openly available. Importantly, the comparative analysis reveals that the existing methods perform well only in detecting one or two types of anomalies out of the three types studied. In contrast, GAD-NR excels at detecting all three types of anomalies across the datasets, demonstrating its comprehensive anomaly detection capabilities.

翻译：图异常检测（GAD）是一种用于识别图中异常节点的技术，广泛应用于网络安全、欺诈检测、社交媒体垃圾信息检测等多个领域。GAD的常见方法之一是图自编码器（GAEs），它通过将图数据编码为节点表示，并基于这些表示评估图的重构质量来识别异常。然而，现有的GAE模型主要针对直接链接重构进行优化，导致图中相连的节点在潜在空间中聚集。因此，这类模型擅长检测聚类型结构异常，但在处理不符合聚类模式的复杂结构异常时表现欠佳。为解决这一局限，我们提出了一种名为GAD-NR的新型解决方案，它是GAE的变体，通过引入邻域重构实现图异常检测。GAD-NR旨在基于节点表示重构节点的完整邻域，包括局部结构、自身属性及邻居属性。通过比较异常节点与正常节点之间的邻域重构损失，GAD-NR能够有效检测各类异常。在六个真实数据集上的大量实验验证了GAD-NR的有效性，其在AUC指标上相较于最先进的对比方法实现了高达30%的显著提升。GAD-NR的源代码已公开。重要的是，对比分析表明，现有方法仅能有效检测所研究的三类异常中的一类或两类，而GAD-NR在所有数据集上均能出色检测全部三类异常，展现了其全面的异常检测能力。