Bird flock effect-based dynamic community detection: Unravelling network patterns over time
Community structure is essential for topological analysis, function study, and pattern detection in complex networks. As establishing community structure in a dynamic network is difficult, it gives a unique perspective in many interdisciplinary fields. Many researchers have explored the challenging...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Alexandria Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016824012626 |
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| author | Siti Haryanti Hairol Anuar Zuraida Abal Abas Iskandar Waini Mohd Fariduddin Mukhtar Zejun Sun Eko Arip Winanto Norhazwani Mohd Yunos |
| author_facet | Siti Haryanti Hairol Anuar Zuraida Abal Abas Iskandar Waini Mohd Fariduddin Mukhtar Zejun Sun Eko Arip Winanto Norhazwani Mohd Yunos |
| author_sort | Siti Haryanti Hairol Anuar |
| collection | DOAJ |
| description | Community structure is essential for topological analysis, function study, and pattern detection in complex networks. As establishing community structure in a dynamic network is difficult, it gives a unique perspective in many interdisciplinary fields. Many researchers have explored the challenging technique that requires parameter specification and optimization for quality result. This study proposed an eco-system conceptual framework based on bird flock effect. Relying on the natural law of rule, we designed a dynamic community detection named DCDBFE. The design of algorithm was based on the three basic rules of bird flock: separation, alignment, and cohesion phase. Then, we provide an explanation of similarity measure used between vertices to identify the modules attraction. DCDBFE employs an incremental community detection approach to repeatedly detect communities in each network snapshot or time step. The contributions are obtained for high quality community detected, free-parameter and well stability. To test its performance, extensive experiments were conducted on both synthetic and real-world networks. The outcomes demonstrate that our approach can effectively find satisfaction from each time step by comparison with the other well-known algorithms. |
| format | Article |
| id | doaj-art-afb62d64aa9941238e8c711df806689b |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-afb62d64aa9941238e8c711df806689b2025-01-29T05:00:11ZengElsevierAlexandria Engineering Journal1110-01682025-01-01112177208Bird flock effect-based dynamic community detection: Unravelling network patterns over timeSiti Haryanti Hairol Anuar0Zuraida Abal Abas1Iskandar Waini2Mohd Fariduddin Mukhtar3Zejun Sun4Eko Arip Winanto5Norhazwani Mohd Yunos6Fakulti Teknologi Dan Kejuruteraan Elektronik Dan Komputer (FTKEK), Universiti Teknikal Malaysia Melaka (UTeM), Hang Tuah Jaya, Durian Tunggal, Melaka 76100, Malaysia; Corresponding author.Fakulti Teknologi Maklumat Dan Komunikasi (FTMK), Universiti Teknikal Malaysia Melaka (UTeM), Hang Tuah Jaya, Durian Tunggal, Melaka 76100, MalaysiaFakulti Teknologi Dan Kejuruteraan Industri Dan Pembuatan (FTKIP), Universiti Teknikal Malaysia Melaka (UTeM), Hang Tuah Jaya, Durian Tunggal, Melaka 76100, MalaysiaFakulti Teknologi Dan Kejuruteraan Mekanikal (FTKM), Universiti Teknikal Malaysia Melaka (UTeM), Hang Tuah Jaya, Durian Tunggal, Melaka 76100, MalaysiaSchool of Information Engineering, Pingdingshan University, Henan 467000, ChinaComputer Engineering, Dinamika Bangsa University, Jambi 36138, IndonesiaFakulti Teknologi Maklumat Dan Komunikasi (FTMK), Universiti Teknikal Malaysia Melaka (UTeM), Hang Tuah Jaya, Durian Tunggal, Melaka 76100, MalaysiaCommunity structure is essential for topological analysis, function study, and pattern detection in complex networks. As establishing community structure in a dynamic network is difficult, it gives a unique perspective in many interdisciplinary fields. Many researchers have explored the challenging technique that requires parameter specification and optimization for quality result. This study proposed an eco-system conceptual framework based on bird flock effect. Relying on the natural law of rule, we designed a dynamic community detection named DCDBFE. The design of algorithm was based on the three basic rules of bird flock: separation, alignment, and cohesion phase. Then, we provide an explanation of similarity measure used between vertices to identify the modules attraction. DCDBFE employs an incremental community detection approach to repeatedly detect communities in each network snapshot or time step. The contributions are obtained for high quality community detected, free-parameter and well stability. To test its performance, extensive experiments were conducted on both synthetic and real-world networks. The outcomes demonstrate that our approach can effectively find satisfaction from each time step by comparison with the other well-known algorithms.http://www.sciencedirect.com/science/article/pii/S1110016824012626Network structureDynamic community detectionBird flock effectSimilarity measure |
| spellingShingle | Siti Haryanti Hairol Anuar Zuraida Abal Abas Iskandar Waini Mohd Fariduddin Mukhtar Zejun Sun Eko Arip Winanto Norhazwani Mohd Yunos Bird flock effect-based dynamic community detection: Unravelling network patterns over time Alexandria Engineering Journal Network structure Dynamic community detection Bird flock effect Similarity measure |
| title | Bird flock effect-based dynamic community detection: Unravelling network patterns over time |
| title_full | Bird flock effect-based dynamic community detection: Unravelling network patterns over time |
| title_fullStr | Bird flock effect-based dynamic community detection: Unravelling network patterns over time |
| title_full_unstemmed | Bird flock effect-based dynamic community detection: Unravelling network patterns over time |
| title_short | Bird flock effect-based dynamic community detection: Unravelling network patterns over time |
| title_sort | bird flock effect based dynamic community detection unravelling network patterns over time |
| topic | Network structure Dynamic community detection Bird flock effect Similarity measure |
| url | http://www.sciencedirect.com/science/article/pii/S1110016824012626 |
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