Succinct Link Transformation-Based Overlapping Community Detection Framework for Social Network Analysis
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 it...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11015499/ |
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| author | Seungwoo Ryu Sungsu Lim Seungsoo Yoo Sun Yong Kim |
| author_facet | Seungwoo Ryu Sungsu Lim Seungsoo Yoo Sun Yong Kim |
| author_sort | Seungwoo Ryu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-7725f9e060ba4510a0a35dc38cf1e451 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-7725f9e060ba4510a0a35dc38cf1e4512025-08-20T03:28:10ZengIEEEIEEE Access2169-35362025-01-011310953910955210.1109/ACCESS.2025.357329311015499Succinct Link Transformation-Based Overlapping Community Detection Framework for Social Network AnalysisSeungwoo Ryu0https://orcid.org/0009-0004-8107-3216Sungsu Lim1https://orcid.org/0000-0001-5924-3398Seungsoo Yoo2https://orcid.org/0000-0002-8648-1540Sun Yong Kim3https://orcid.org/0000-0002-4192-2146Defense Rapid Acquisition Technology Research Institute, Agency for Defense Development, Seoul, Republic of KoreaDepartment of Computer Science and Engineering, Chungnam National University, Daejeon, Republic of KoreaDepartment of Electrical and Electronics Engineering, Konkuk University, Seoul, Republic of KoreaDepartment of Electrical and Electronics Engineering, Konkuk University, Seoul, Republic of KoreaDetecting 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.https://ieeexplore.ieee.org/document/11015499/Social network analysisoverlapping community detectionsuccinct link transformationoverlapping community detection frameworkgraph representationcommunity detection algorithms |
| spellingShingle | Seungwoo Ryu Sungsu Lim Seungsoo Yoo Sun Yong Kim Succinct Link Transformation-Based Overlapping Community Detection Framework for Social Network Analysis IEEE Access Social network analysis overlapping community detection succinct link transformation overlapping community detection framework graph representation community detection algorithms |
| title | Succinct Link Transformation-Based Overlapping Community Detection Framework for Social Network Analysis |
| title_full | Succinct Link Transformation-Based Overlapping Community Detection Framework for Social Network Analysis |
| title_fullStr | Succinct Link Transformation-Based Overlapping Community Detection Framework for Social Network Analysis |
| title_full_unstemmed | Succinct Link Transformation-Based Overlapping Community Detection Framework for Social Network Analysis |
| title_short | Succinct Link Transformation-Based Overlapping Community Detection Framework for Social Network Analysis |
| title_sort | succinct link transformation based overlapping community detection framework for social network analysis |
| topic | Social network analysis overlapping community detection succinct link transformation overlapping community detection framework graph representation community detection algorithms |
| url | https://ieeexplore.ieee.org/document/11015499/ |
| work_keys_str_mv | AT seungwooryu succinctlinktransformationbasedoverlappingcommunitydetectionframeworkforsocialnetworkanalysis AT sungsulim succinctlinktransformationbasedoverlappingcommunitydetectionframeworkforsocialnetworkanalysis AT seungsooyoo succinctlinktransformationbasedoverlappingcommunitydetectionframeworkforsocialnetworkanalysis AT sunyongkim succinctlinktransformationbasedoverlappingcommunitydetectionframeworkforsocialnetworkanalysis |