Dual Structural Consistency Preserving Community Detection on Social Networks

Abstract

Community detection on social networks is a fundamental and crucial task in the research field of social computing. Here we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DSCPCD</i> —a dual structural consistency preserving community detection method to uncover the hidden community structure, which is designed regarding two criteria: 1) users interact with each other in a manner combining uncertainty and certainty; 2) original explicit network (two linked users are friends) and potential implicit network (two linked users have common friends) should have a consistent community structure, i.e., <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dual structural consistency</i> . Particularly, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DSCPCD</i> formulates each user in a social network as an individual in an evolutionary game associated with community-aware payoff settings, where the community state evolves under the guidance of replicator dynamics. To further seek each user's membership, we develop a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">happiness</i> index to measure all users' satisfaction towards two community structures in explicit and implicit networks, meanwhile, the dual community structural consistency between the two networks is also characterized. Specifically, each user is assumed to maximize the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">happiness</i> bounded by the evolutionary community state. We evaluate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DSCPCD</i> on several real-world and synthetic datasets, and the results show that it can yield substantial performance gains in terms of detection accuracy over several baselines.