A fast community detection method in bipartite networks by distance dynamics
Many real bipartite networks are found to be divided into two-mode communities. In this paper, we formulate a new two-mode community detection algorithm BiAttractor. It is based on distance dynamics model Attractor proposed by Shao et al. with extension from unipartite to bipartite networks. Since J...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2018
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| Subjects: | |
| Online Access: | https://eprints.nottingham.ac.uk/48844/ |
| _version_ | 1848797861734514688 |
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| author | Sun, Hong-Liang Ch'ng, Eugene Yong, Xi Garibaldi, Jonathan M. See, Simon Chen, Duan-Bing |
| author_facet | Sun, Hong-Liang Ch'ng, Eugene Yong, Xi Garibaldi, Jonathan M. See, Simon Chen, Duan-Bing |
| author_sort | Sun, Hong-Liang |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Many real bipartite networks are found to be divided into two-mode communities. In this paper, we formulate a new two-mode community detection algorithm BiAttractor. It is based on distance dynamics model Attractor proposed by Shao et al. with extension from unipartite to bipartite networks. Since Jaccard coefficient of distance dynamics model is incapable to measure distances of different types of vertices in bipartite networks, our main contribution is to extend distance dynamics model from unipartite to bipartite networks using a novel measure Local Jaccard Distance (LJD). Furthermore, distances between different types of vertices are not affected by common neighbors in the original method. This new idea makes clear assumptions and yields interpretable results in linear time complexity O(jEj) in sparse networks, where jEj is the number of edges. Experiments on synthetic networks demonstrate it is capable to overcome resolution limit compared with existing other methods. Further research on real networks shows that this model can accurately detect interpretable community structures in a short time. |
| first_indexed | 2025-11-14T20:10:36Z |
| format | Article |
| id | nottingham-48844 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T20:10:36Z |
| publishDate | 2018 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-488442018-12-30T04:30:16Z https://eprints.nottingham.ac.uk/48844/ A fast community detection method in bipartite networks by distance dynamics Sun, Hong-Liang Ch'ng, Eugene Yong, Xi Garibaldi, Jonathan M. See, Simon Chen, Duan-Bing Many real bipartite networks are found to be divided into two-mode communities. In this paper, we formulate a new two-mode community detection algorithm BiAttractor. It is based on distance dynamics model Attractor proposed by Shao et al. with extension from unipartite to bipartite networks. Since Jaccard coefficient of distance dynamics model is incapable to measure distances of different types of vertices in bipartite networks, our main contribution is to extend distance dynamics model from unipartite to bipartite networks using a novel measure Local Jaccard Distance (LJD). Furthermore, distances between different types of vertices are not affected by common neighbors in the original method. This new idea makes clear assumptions and yields interpretable results in linear time complexity O(jEj) in sparse networks, where jEj is the number of edges. Experiments on synthetic networks demonstrate it is capable to overcome resolution limit compared with existing other methods. Further research on real networks shows that this model can accurately detect interpretable community structures in a short time. Elsevier 2018-04-15 Article PeerReviewed application/pdf en https://eprints.nottingham.ac.uk/48844/1/elsarticle-template-num.pdf Sun, Hong-Liang, Ch'ng, Eugene, Yong, Xi, Garibaldi, Jonathan M., See, Simon and Chen, Duan-Bing (2018) A fast community detection method in bipartite networks by distance dynamics. Physica A: Statistical Mechanics and its Applications, 496 . pp. 108-120. ISSN 0378-4371 Node similarity; Community detection; Large bipartite networks https://www.sciencedirect.com/science/article/pii/S0378437117313481 doi:10.1016/j.physa.2017.12.099 doi:10.1016/j.physa.2017.12.099 |
| spellingShingle | Node similarity; Community detection; Large bipartite networks Sun, Hong-Liang Ch'ng, Eugene Yong, Xi Garibaldi, Jonathan M. See, Simon Chen, Duan-Bing A fast community detection method in bipartite networks by distance dynamics |
| title | A fast community detection method in bipartite networks by distance dynamics |
| title_full | A fast community detection method in bipartite networks by distance dynamics |
| title_fullStr | A fast community detection method in bipartite networks by distance dynamics |
| title_full_unstemmed | A fast community detection method in bipartite networks by distance dynamics |
| title_short | A fast community detection method in bipartite networks by distance dynamics |
| title_sort | fast community detection method in bipartite networks by distance dynamics |
| topic | Node similarity; Community detection; Large bipartite networks |
| url | https://eprints.nottingham.ac.uk/48844/ https://eprints.nottingham.ac.uk/48844/ https://eprints.nottingham.ac.uk/48844/ |