TA-Detector: A GNN-Based Anomaly Detector via Trust Relationship

Abstract

With the rise of mobile Internet and AI, social media integrating short messages, images, and videos has developed rapidly. As a guarantee for the stable operation of social media, information security, especially graph anomaly detection (GAD), has become a hot issue inspired by the extensive attention of researchers. Most GAD methods are mainly limited to enhancing the homophily or considering homophily and heterophilic connections. Nevertheless, due to the deceptive nature of homophily connections among anomalies, the discriminative information of the anomalies can be eliminated. To alleviate the issue, we explore a novel method TA-Detector in GAD by introducing the concept of trust into the classification of connections. In particular, the proposed approach adopts a designed trust classier to distinguish trust and distrust connections with the supervision of labeled nodes. Then, we capture the latent factors related to GAD by graph neural networks, which integrate node interaction type information and node representation. Finally, to identify anomalies in the graph, we use the residual network mechanism to extract the deep semantic embedding information related to GAD. Experimental results on two real benchmark datasets verify that our proposed approach boosts the overall GAD performance in comparison to benchmark baselines.