  Community detection is a task of fundamental importance in social network
analysis that can be used in a variety of knowledge-based domains. While there
exist many works on community detection based on connectivity structures, they
suffer from either considering the overlapping or non-overlapping communities.
In this work, we propose a novel approach for general community detection
through an integrated framework to extract the overlapping and non-overlapping
community structures without assuming prior structural connectivity on
networks. Our general framework is based on a primary node based criterion
which consists of the internal association degree along with the external
association degree. The evaluation of the proposed method is investigated
through the extensive simulation experiments and several benchmark real network
datasets. The experimental results show that the proposed method outperforms
the earlier state-of-the-art algorithms based on the well-known evaluation
criteria.
