IEDC: An Integrated Approach for Overlapping and Non-overlapping Community Detection

TL;DR

This paper introduces a novel integrated framework for community detection in social networks that effectively identifies both overlapping and non-overlapping communities without prior structural assumptions, outperforming existing methods.

Contribution

The work presents a new general approach combining internal and external association degrees for community detection, capable of handling both overlapping and non-overlapping structures.

Findings

Outperforms state-of-the-art algorithms in experiments

Effective on various benchmark real network datasets

Validated through extensive simulations

Abstract

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.