Reinforced Contrastive Graph Neural Networks (RCGNN) for Anomaly Detection
Zenan Sun, Jingyi Su, Donghyun Jeon, Alvaro Velasquez, Houbing Herbert Song, Shuteng Niu · 2022
Despite the recent state-of-the-art performance of Deep Learning (DL), imbalanced graph-structured data remains an open challenge in social science, traffic networks, and biomedical informatics. Recently, a surge in research on Representation Learning has significantly improved the performance of DL algorithms on imbalanced non-graph-structured data. In addition, Graph Neural Networks (GNNs) already in widespread use for representing graph-structured data in DL models with more advanced techniques in neural message-passing and deep graph embedding. However, most existing works are based on assumptions that oversimplify the complexity of real-world problems. In this paper, we propose Reinforced Contrastive GNNs (RCGNN), a novel graph representation learning model for anomaly detection with multi-relational graph-structured data. The proposed model produces a neighbor selection with Reinforcement Learning (RL) based on the similarity of neighborhoods in multi-relational structured graphs. In addition, the graph representation is learned by an adaptive AutoEncoder (AE) with Triplet Loss (TL) in Contrastive Learning. By aggregating the nodes with the highest similarities in their features and the importance of each node, our model is able to construct the multi-relational graphs by keeping the complexity of the graph structure as well as the relation-dependency representations. Experiments on multiple benchmark data sets demonstrate the advantage of RCGNN in learning better representations for multi-relational graphs. Furthermore, compared to other GNN models, our model shows better performance in accuracy, F1, and PR AUC scores.