Open AccessSuccinct Link Transformation-based Overlapping Community Detection Framework for Social Network Analysis
Author(s) -
Seungwoo Ryu,
Sungsu Lim,
Seungsoo Yoo,
Sun Yong Kim
Publication year - 2025
Publication title -
ieee access
Language(s) - English
Resource type - Magazines
SCImago Journal Rank - 0.587
H-Index - 127
eISSN - 2169-3536
DOI - 10.1109/access.2025.3573293
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
Detecting communities in real-world networks is a challenging and important problem. Numerous algorithms have been proposed in recent years, and significant progress has been made. Overlapping community detection, where individual nodes may belong to multiple communities, has gained attention for its applicability to real-world scenarios. However, accurately analyzing complex networks with high levels of overlap remains difficult. This study presents a succinct link transformation-based framework for overlapping community detection that addresses the challenges of dense and large-scale networks while supporting scalability. The framework transforms the original network into a succinct line graph, or edge-centric graph, by leveraging both node-node and edge-edge (referred to as “ link ”) relationships. Within this transformed graph, edges and links are prioritized using minwise hashing, resulting in an efficient link transformation method. The proposed framework was evaluated against mainstream overlapping community detection algorithms. Experimental results showed superior performance on dense overlapping graphs, with up to 53% improvement in computational efficiency and up to 67% increase in detection accuracy compared to existing methods, demonstrating both scalability and extensibility. This framework enables high-quality overlapping community detection on dense and large-scale real-world networks using various existing algorithms.