Community Search over Big Graphs: Models, Algorithms, and Opportunities

Abstract

Communities serve as basic structures for understanding the organization of many real-world networks, such as social, biological, collaboration, and communication networks. Recently, community search over large graphs has attracted significantly increasing attention, from simple and static graphs to evolving, attributed, location-based graphs. Different from the well-studied problem of community detection that finds all communities in an entire network, community search is to find the cohesive communities w.r.t. the query nodes. In this tutorial, we survey the state-of-the-art of community search on various kinds of networks across different application areas such as densely-connected community search, attributed community search, social circle discovery, and querying geosocial groups. We first highlight the challenges posed by the community search problems. We continue the presentation of their principles, methodologies, algorithms, and applications, and give a comprehensive comparison of the state-of-the-art techniques. This tutorial finally concludes by offering future directions for research in this important and growing area.