LB-SAM: Local Beam Search With Simulated Annealing for Community Detection in Large-Scale Social Networks
With the rapid development of internet technologies and the increasing availability of large-scale data, the detection of community structures within complex networks has become a critical area of research. This paper introduces a novel community detection technique called <monospace>LB-SAM<...
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10752532/ |
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| author | Keshab Nath Rupam Kumar Sharma SK Mahmudul Hassan |
| author_facet | Keshab Nath Rupam Kumar Sharma SK Mahmudul Hassan |
| author_sort | Keshab Nath |
| collection | DOAJ |
| description | With the rapid development of internet technologies and the increasing availability of large-scale data, the detection of community structures within complex networks has become a critical area of research. This paper introduces a novel community detection technique called <monospace>LB-SAM</monospace> (Local Beam Search with Simulated Annealing and Modularity), designed to efficiently uncover hidden community structures in large-scale social networks. <monospace>LB-SAM</monospace> integrates Local Beam Search (LBS) to explore the local network structure and Simulated Annealing (SA) to globally optimize modularity, enabling the detection of communities with intricate boundaries and strong internal connections. By focusing on influential nodes to form subgroups and recursively merging them based on modularity, LB-SAM provides superior scalability and robustness in both real-world and synthetic networks. Extensive experiments conducted on 12 real-world and 6 synthetic datasets demonstrate that LB-SAM consistently outperforms existing state-of-the-art algorithms, particularly in networks with unclear community structures, and scales effectively to billion-scale networks. The proposed method has wide-ranging applications in sociology, biology, marketing, and cybersecurity, offering valuable insights into the structure and dynamics of large social networks. |
| format | Article |
| id | doaj-art-42fad6f5e062426b944fc6a169f9dd34 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-42fad6f5e062426b944fc6a169f9dd342024-11-22T00:02:01ZengIEEEIEEE Access2169-35362024-01-011216770516772310.1109/ACCESS.2024.349721610752532LB-SAM: Local Beam Search With Simulated Annealing for Community Detection in Large-Scale Social NetworksKeshab Nath0Rupam Kumar Sharma1SK Mahmudul Hassan2https://orcid.org/0000-0002-3714-9453Department of Computer Science and Engineering, Bhattadev University, Bajali, Pathsala, IndiaDepartment of Computer Science and Engineering, Rajiv Gandhi University, Doimukh, Arunachal Pradesh, IndiaDepartment of Information Technology, Manipal Academy of Higher Education, Manipal Institute of Technology Bengaluru, Manipal, Karnataka, IndiaWith the rapid development of internet technologies and the increasing availability of large-scale data, the detection of community structures within complex networks has become a critical area of research. This paper introduces a novel community detection technique called <monospace>LB-SAM</monospace> (Local Beam Search with Simulated Annealing and Modularity), designed to efficiently uncover hidden community structures in large-scale social networks. <monospace>LB-SAM</monospace> integrates Local Beam Search (LBS) to explore the local network structure and Simulated Annealing (SA) to globally optimize modularity, enabling the detection of communities with intricate boundaries and strong internal connections. By focusing on influential nodes to form subgroups and recursively merging them based on modularity, LB-SAM provides superior scalability and robustness in both real-world and synthetic networks. Extensive experiments conducted on 12 real-world and 6 synthetic datasets demonstrate that LB-SAM consistently outperforms existing state-of-the-art algorithms, particularly in networks with unclear community structures, and scales effectively to billion-scale networks. The proposed method has wide-ranging applications in sociology, biology, marketing, and cybersecurity, offering valuable insights into the structure and dynamics of large social networks.https://ieeexplore.ieee.org/document/10752532/Local beam searchsimulated annealingmodularitylocal intrinsic densitycommunity detectiononline social networks |
| spellingShingle | Keshab Nath Rupam Kumar Sharma SK Mahmudul Hassan LB-SAM: Local Beam Search With Simulated Annealing for Community Detection in Large-Scale Social Networks IEEE Access Local beam search simulated annealing modularity local intrinsic density community detection online social networks |
| title | LB-SAM: Local Beam Search With Simulated Annealing for Community Detection in Large-Scale Social Networks |
| title_full | LB-SAM: Local Beam Search With Simulated Annealing for Community Detection in Large-Scale Social Networks |
| title_fullStr | LB-SAM: Local Beam Search With Simulated Annealing for Community Detection in Large-Scale Social Networks |
| title_full_unstemmed | LB-SAM: Local Beam Search With Simulated Annealing for Community Detection in Large-Scale Social Networks |
| title_short | LB-SAM: Local Beam Search With Simulated Annealing for Community Detection in Large-Scale Social Networks |
| title_sort | lb sam local beam search with simulated annealing for community detection in large scale social networks |
| topic | Local beam search simulated annealing modularity local intrinsic density community detection online social networks |
| url | https://ieeexplore.ieee.org/document/10752532/ |
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