AnomMAN: Detect Anomaly on Multi-view Attributed Networks
This addresses the problem of detecting anomalies in complex networks with multiple interaction types, such as in online shopping or finance, though it appears incremental as it builds on existing graph-based methods.
The paper tackles anomaly detection on multi-view attributed networks by proposing AnomMAN, a graph convolution-based framework that uses attention to integrate multiple interaction types and a graph auto-encoder to leverage low-pass features, achieving state-of-the-art performance on real-world datasets.
Anomaly detection on attributed networks is widely used in online shopping, financial transactions, communication networks, and so on. However, most existing works trying to detect anomalies on attributed networks only consider a single kind of interaction, so they cannot deal with various kinds of interactions on multi-view attributed networks. It remains a challenging task to jointly consider all different kinds of interactions and detect anomalous instances on multi-view attributed networks. In this paper, we propose a graph convolution-based framework, named AnomMAN, to detect Anomaly on Multi-view Attributed Networks. To jointly consider attributes and all kinds of interactions on multi-view attributed networks, we use the attention mechanism to define the importance of all views in networks. Since the low-pass characteristic of graph convolution operation filters out most high-frequency signals (aonmaly signals), it cannot be directly applied to anomaly detection tasks. AnomMAN introduces the graph auto-encoder module to turn the disadvantage of low-pass features into an advantage. According to experiments on real-world datasets, AnomMAN outperforms the state-of-the-art models and two variants of our proposed model.