IEDC: An Integrated Approach for Overlapping and Non-overlapping Community Detection
This addresses a limitation in social network analysis where existing methods typically handle only overlapping or non-overlapping communities, but it appears incremental as it integrates these aspects without a major paradigm shift.
The paper tackled the problem of detecting both overlapping and non-overlapping communities in networks without prior structural assumptions, and the results showed that the proposed method outperformed earlier state-of-the-art algorithms based on evaluation criteria.
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.