Mining Query-Based Subnetwork Outliers in Heterogeneous Information Networks

Mining outliers in a heterogeneous information network is a challenging problem: It is even unclear what should be outliers in a large heterogeneous network (e.g., outliers in the entire bibliographic network consisting of authors, titles, papers and venues). In this study, we propose an interesting class of outliers, query-based subnetwork outliers: Given a heterogeneous network, a user raises a query to retrieve a set of task-relevant subnetworks, among which, subnetwork outliers are those that significantly deviate from others (e.g., outliers of author groups among those studying "topic modeling"). We formalize this problem and propose a general framework, where one can query for finding subnetwork outliers with respect to different semantics. We introduce the notion of subnetwork similarity that captures the proximity between two subnetworks by their membership distributions. We propose an outlier detection algorithm to rank all the subnetworks according to their outlierness without tuning parameters. Our quantitative and qualitative experiments on both synthetic and real data sets show that the proposed method outperforms other baselines.