Anomaly Detection on Attributed Networks via Multiview and Multiscale Contrastive Learning

Detecting abnormal nodes from attributed networks plays an important role in various applications, including cybersecurity, finance, and social networks. Most existing methods focus on learning different scales of graphs or using augmented data to improve the quality of feature representation. However, the performance is limited due to two critical problems. First, the high sensitivity of attributed networks makes it uncontrollable and uncertain to use conventional methods for data augmentation, leading to limited improvement in representation and generalization capabilities. Second, under the unsupervised paradigm, anomalous nodes mixed in the training data may interfere with the learning of normal patterns and weaken the discrimination ability. In this work, we propose a novel multiview and multiscale contrastive learning framework to address these two issues. Specifically, a network augmentation method based on parameter perturbation is introduced to generate augmented views for both nodea-node and node-subgraph level contrast branches. Then, cross-view graph contrastive learning is employed to improve the representation without the need for augmented data. We also provide a cycle training strategy where normal samples detected in the former step are collected for an additional training step. In this way, the ability to learn normal patterns is enhanced. Extensive experiments on six benchmark datasets demonstrate that our method outperforms the existing state-of- the-art baselines.

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