ANEMONE: Graph Anomaly Detection with Multi-Scale Contrastive Learning

Abstract

Anomaly detection on graphs plays a significant role in various domains, including cybersecurity, e-commerce, and financial fraud detection. However, existing methods on graph anomaly detection usually consider the view in a single scale of graphs, which results in their limited capability to capture the anomalous patterns from different perspectives. Towards this end, we introduce a novel graph anomaly detection framework, namely ANEMONE, to simultaneously identify the anomalies in multiple graph scales. Concretely, ANEMONE first leverages a graph neural network backbone encoder with multi-scale contrastive learning objectives to capture the pattern distribution of graph data by learning the agreements between instances at the patch and context levels concurrently. Then, our method employs a statistical anomaly estimator to evaluate the abnormality of each node according to the degree of agreement from multiple perspectives.

Ming Jin

Lecturer of AI

My research interests include machine learning and artificial intelligence.

Yixin Liu

ARC Research Fellow

My research interests include machine learning, graph analysis and audio processing.

Shirui Pan

Professor and ARC Future Fellow

My research interests include data mining, machine learning, and graph analysis.