Learning Node Abnormality with Weak Supervision

Graph anomaly detection aims to identify the atypical substructures and has attracted an increasing amount of research attention due to its profound impacts in a variety of application domains, including social network analysis, security, finance, and many more. The lack of prior knowledge of the ground-truth anomaly has been a major obstacle in acquiring fine-grained annotations (e.g., anomalous nodes), therefore, a plethora of existing methods have been developed either with a limited number of node-level supervision or in an unsupervised manner. Nonetheless, annotations for coarse-grained graph elements (e.g., a suspicious group of nodes), which often require marginal human effort in terms of time and expertise, are comparatively easier to obtain. Therefore, it is appealing to investigate anomaly detection in a weakly-supervised setting and to establish the intrinsic relationship between annotations at different levels of granularity. In this paper, we tackle the challenging problem of weakly-supervised graph anomaly detection with coarse-grained supervision by (1) proposing a novel architecture of graph neural network with attention mechanism named Wedge that can identify the critical node-level anomaly given a few labels of anomalous subgraphs, and (2) designing a novel objective with contrastive loss that facilitates node representation learning by enforcing distinctive representations between normal and abnormal graph elements. Through extensive evaluations on real-world datasets, we corroborate the efficacy of our proposed method, improving AUC-ROC by up to 16.48% compared to the best competitor.