Searching Target Communities with Outliers in attributed graph

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.