PERTAD: Towards Pseudo Verification for Anomaly Detection in Partially Labeled Graphs

Wenjing Chang, Jianjun Yu, Xiaojun Zhou

Abstract: The graph-based anomaly detection task aims to identify nodes with patterns that deviate from those of the majority nodes in a large graph, where only a limited subset of nodes is annotated. However, inadequate supervised knowledge and uncertainty of anomalous structure restrict the performance of detection. In this paper, we propose PERTAD (Pseudo VERificaTion for Anomaly Detection), a novel semi-supervised learning method for detecting anomalies in partially labeled graphs. Specifically, we first propose a self-verification framework that comprises a target network (T-model) and a verification network (V-model). The framework employs pseudo-labeled graphs to transfer the knowledge learned by T-model to V-model, and then corrects T-model with the performance error between the two networks on the labeled set. Furthermore, we introduce a layer-level aggregation mechanism for node representation in deep GNNs to address the uncertainty of anomalous structures. The proposed mechanism re-aggregates neighborhood information across layers, aiming to preserve low-order neighborhood characteristics and alleviate the over-smoothing effect. Extensive experiments on real-world graph-based anomaly detection tasks demonstrate that PERTAD significantly outperforms state-of-the-art baselines.

External IDs:dblp:conf/apweb/ChangYZ23