Leveraging User Preferences for Community Search via Attribute Subspace

Abstract

In this paper, we propose a community search scheme via attribute subspace. This method utilizes not only network structure but also node attributes within a certain subspace to quantify a community from the perspective of both internal consistency and external separability, which is able to capture a user preferred community. Firstly, the attributes similarity and neighborhood information of nodes are combined, and the center node set of the target community can be obtained by extending the sample node given by the user with its neighbors. Secondly, an attribute subspace calculation method with entropy weights is established based on the center node set, and the attribute subspace of the community can thus be deduced. Finally, the community quality, which is the combination of internal connectivity and external separability is defined, based on which the target community with user’s preference can be detected. Experimental results on both synthetic network and real-world network datasets demonstrated the efficiency and effectiveness of the proposed algorithm.