The task of graph-level anomaly detection (GLAD) is to identify anomalous graphs that deviate significantly from the majority of graphs in a dataset. While deep GLAD methods have shown promising performance, their black-box nature limits their reliability and deployment in real-world applications. Although some recent methods have made attempts to provide explanations for anomaly detection results, they either provide explanations without referencing normal graphs, or rely on abstract latent vectors as prototypes rather than concrete graphs from the dataset. To address these limitations, we propose Prototype-based Graph-Level Anomaly Detection (ProtoGLAD), an interpretable unsupervised framework that provides explanation for each detected anomaly by explicitly contrasting with its nearest normal prototype graph. It employs a point-set kernel to iteratively discover multiple normal prototype graphs and their associated clusters from the dataset, then identifying graphs distant from all discovered normal clusters as anomalies. Extensive experiments on multiple real-world datasets demonstrate that ProtoGLAD achieves competitive anomaly detection performance compared to state-of-the-art GLAD methods while providing better human-interpretable prototype-based explanations.

翻译：图级异常检测（GLAD）的任务是识别与数据集中大多数图显著偏离的异常图。尽管深度GLAD方法已展现出优异的性能，但其黑盒特性限制了在实际应用中的可靠性和部署。虽然近期部分方法尝试为异常检测结果提供解释，但它们要么在不参考正常图的情况下提供解释，要么依赖抽象的潜在向量作为原型而非数据集中的具体图。为解决这些局限性，我们提出基于原型的图级异常检测（ProtoGLAD），这是一个可解释的无监督框架，通过将每个检测到的异常与其最近邻的正常原型图进行显式对比来提供解释。该方法采用点集核函数从数据集中迭代发现多个正常原型图及其关联簇，进而将远离所有已发现正常簇的图识别为异常。在多个真实数据集上的大量实验表明，与最先进的GLAD方法相比，ProtoGLAD在实现具有竞争力的异常检测性能的同时，能提供更易于人类理解的基于原型的解释。