Unsupervised Deep Subgraph Anomaly Detection

Abstract

Effectively mining anomalous subgraphs in networks is crucial for many application scenarios, such as disease outbreak detection, financial fraud detection, and activity monitoring in social networks. Identifying anomalous subgraphs is extremely challenging due to their complex topological structures and high-dimensional attributes, various notions of anomalies, and the exponentially large subgraph space in a given graph. Existing classical shallow models typically rely on handcrafted anomaly measure functions, which cannot handle common situations when such prior knowledge is unavailable. Recently, deep learning-based methods provide an end-to-end way that learns the anomaly measure functions. However, although they have achieved great success in detecting node-level, edge-level, and graph-level anomalies, detecting anomalous at the subgraph level has been largely under-explored due to enormous difficulties in subgraph representation learning, supervision, and end-to-end anomaly quantification. To circumvent the above mentioned challenges, this paper proposes a novel deep framework named Anomalous Subgraph Autoencoder (AS-GAE) to extract the anomalous subgraphs in an unsupervised and weakly supervised manner. Specifically, we first develop a location-aware graph auto-encoder to uncover the anomalous areas in the given graph according to the mismatch during the reconstruction. Then a supermodular graph scoring function module is proposed to assign reasonable anomaly scores to the subgraphs in the extracted anomalous areas. The superiority of our proposed method was demonstrated through extensive experiments on two synthetic datasets and nine real-world datasets.