A Deep Multi-View Framework for Anomaly Detection on Attributed Networks (Extended Abstract)

Many existing anomaly detection methods on attributed networks do not seriously tackle the inherent multi-view property in attribute space but concatenate multiple views into a single feature vector, which inevitably ignores the incompatibility between heterogeneous views caused by their own statistical properties. In practice, the distinct but complementary information brought by multi-view data promises the potential for more effective anomaly detection than the efforts only based on single-view data. Furthermore, abnormal patterns naturally behave diversely in different views, which coincides with people's desire to discover specific abnormalities according to their preferences for views (attributes). Most existing methods cannot adapt to people's requirements as they fail to consider the idiosyncrasy of user preferences. Thus, in this paper, we propose a multi-view framework ALARM to incorporate user preferences into anomaly detection and simultaneously tackle heterogeneous attribute characteristics through multiple graph encoders and a well-designed aggregator that supports self-learning and user-guided learning. Experiments on synthetic and real-world datasets corroborate the desirable performance of ALARM and its effectiveness in supporting user-oriented anomaly detection.