Searching Target Communities with Outliers in attributed graph

Abstract

Classical community search methods aim to detect local communities containing a set of sample nodes provided by users, which have been wildly studied in recent years. Existing efforts on community search have mainly detected communities where the sample nodes are located. Nevertheless, they may fail to capture communities without sample nodes but are similar with user’s preference deduced from the given sample nodes. We argue that community search should take user’s preference into account during searching process, steering the algorithm to capture more interesting parts of the entire attributed graph.In this work, we propose a community search model that is capable of finding multiple target communities with few given sample nodes and simultaneously identify outliers in attributed network. The model is termed as Searching Target Communities with Outliers (STCO), which collaborates user’s preference into the process of searching to find interesting clusters of the entire network. Particularly, we specify two STCO methods, named STCOE and STCOT, based on two strategies of exploring sample node candidates, respectively. The average partition similarity is defined on the expanded candidate node set to infer the attribute subspace as user’s latent interest. And then, multiple communities and outliers in the whole network are detected via fractional-core and structural constraints. We demonstrate the effectiveness and efficiency of our model on several synthetic and real-world attributed networks with different scales and subjects.