Unsupervised Anomaly Detection on Attributed Multiplex Heterogeneous Networks

The network data model can effectively capture relationships between entities, making it useful for applications such as social networks and e-commerce systems. Anomaly detection, a critical task in network analysis, focuses on identifying unusual patterns that deviate from expected behavior and plays a key role in enhancing the reliability of decision-making processes. While most existing studies focus on detecting anomalies in homogeneous networks, modern applications often involve more complex networks characterized by heterogeneity (nodes and edges of different types), diversified node attributes, and edge multiplicity (node pairs with various edges). Motivated by these practical complexities, this paper investigates anomaly detection in Attributed Multiplex Heterogeneous Networks (AMHENs), which remains challenging due to two significant gaps: the seamless integration of AMHENs’ characteristics and the comprehensive assessment of the diverse anomalies. To close these gaps, we introduce a novel Augmentation-based Multi-channel Graph convolutional network (AMG) that incorporates integrated learning and anomaly scoring mechanisms. Firstly, a multi-channel graph convolutional network is devised to capture the diverse characteristics, which can deeply learn local and global information from AMHENs and aggregate them. Secondly, to comprehensively assess varios types of anomalies, we design a hierarchical network augmentation module and an integrated model optimization module to enable the model to score anomalies through the learning of multiple views. Extensive experiments demonstrate that AMG significantly outperforms competing baselines, achieving improvements of 17.4% and 20.6% over the best-performing baselines on the widely-used Amazon and Yelp datasets, respectively.