Multipattern Integrated Networks With Contrastive Pretraining for Graph Anomaly Detection

Abstract

As a challenge of practical significance, fraud detection has great potential for telecom fraud prevention, economic crime prevention, and personal property preservation. Fraudulent activities are always buried in massive regular transactions, making it hard to find them. Traditional rule-based approaches need multiple domain-specific rules and multistep verification, which limits their transferability and efficiency. Machine learning-based methods might ignore the intricate interactions or the temporal relations among accounts. Meanwhile, the lack of sufficient manual labels restricts their performance. To overcome the above limitations, we present a multipattern integrated network (MPIN) in this article to identify fraudulent accounts in transaction networks. Specifically, MPIN considers the interactions among nodes from three perspectives: inflows, outflows, and their mutual influences. To learn the behavior pattern of each node, MPIN first applies an attention mechanism to integrate the short-term information and then learns the long-term patterns by aggregating multiple short-term patterns. Behavior patterns from different perspectives together with long short-term modeling enable the model to precisely distinguish fraudulent accounts from the normal ones. Moreover, contrastive pretraining with temporal consistency and local tightness guarantee is adopted to alleviate the label sparsity issue and provide the model with low-variance performance. We conducted experiments on two real-world transaction networks, and the results showed the effectiveness of MPIN compared with five state-of-the-art baselines.