Title: Finding Attribute Diversified Communities in Complex Networks
Authors: Afzal Azeem Chowdhary, Chengfei Liu, Lu Chen, Rui Zhou and Yun Yang
Abstract: Recently, finding communities by considering both structure cohesiveness and attribute cohesiveness has begun to generate considerable interest. However, existing works only consider attribute cohesiveness from the perspective of attribute similarity. No work has considered finding communities with attribute diversity, which has good use in many applications. In this paper, we study the problem of searching attribute diversified communities in complex networks. We propose a model for attribute diversified communities and investigate the problem of attribute diversified community search based on k-core. We first prove the NPhardness of the problem, and then propose efficient branch and bound algorithms with novel effective bounds. The experiments performed on various complex network datasets demonstrate the efficiency and effectiveness of our algorithms for finding attribute diversified communities and entitle the significance of our study.
