  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.
