Community detection is a task of fundamental importance in social networkanalysis that can be used in a variety of knowledge-based domains. While thereexist many works on community detection based on connectivity structures, theysuffer from either considering the overlapping or non-overlapping communities.In this work, we propose a novel approach for general community detectionthrough an integrated framework to extract the overlapping and non-overlappingcommunity structures without assuming prior structural connectivity onnetworks. Our general framework is based on a primary node based criterionwhich consists of the internal association degree along with the externalassociation degree. The evaluation of the proposed method is investigatedthrough the extensive simulation experiments and several benchmark real networkdatasets. The experimental results show that the proposed method outperformsthe earlier state-of-the-art algorithms based on the well-known evaluationcriteria.
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