RCS: An Attributed Community Search Approach Based on Representation Learning

Community search, also known as local community detection, is designed to obtain the community structure containing the given query nodes. At present, community search technology has been widely studied, but most of them ignore the effect of node attributes. Some studies propose attributed community search methods and usually adopt a two-phase approach. Generally, the subgraph with structure cohesiveness is obtained first and then the subgraph with keyword cohesiveness is returned as the final result on this basis. In practice, approaches of this kind may lead to incomplete or incorrect results, because information from one single aspect cannot accurately describe the feature of nodes. That is, topology information and attribute information of the network should be complementary. In this paper, we propose an attributed community model called RCS(Representation-based Community Search). Firstly we apply random-walk based representation learning technology to combine topology information and attribute information of nodes. Secondly we propose a new community model to formulate the problem that what is a good community based on the representation information. Then a series of algorithms are designed to fetch a cohesive community whose vertices are tightly connected and closely related to query nodes. Finally the effectiveness and efficiency of RCS are verified by extensive experiments on several real-world networks with ground-truth cnmmunities.