Automatic Detection of Multilevel Communities: Scalable, Selective and Resolution-Limit-Free
Community structure is one of the most important features of complex networks. Modularity-based methods for community detection typically rely on heuristic algorithms to optimize a specific community quality function. Such methods have two major limits: (1) the resolution limit problem, which prohib...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/3/1774 |
| _version_ | 1797625141351415808 |
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| author | Kun Gao Xuezao Ren Lei Zhou Junfang Zhu |
| author_facet | Kun Gao Xuezao Ren Lei Zhou Junfang Zhu |
| author_sort | Kun Gao |
| collection | DOAJ |
| description | Community structure is one of the most important features of complex networks. Modularity-based methods for community detection typically rely on heuristic algorithms to optimize a specific community quality function. Such methods have two major limits: (1) the resolution limit problem, which prohibits communities of heterogeneous sizes being simultaneously detected, and (2) divergent outputs of the heuristic algorithm, which make it difficult to differentiate relevant and irrelevant results. In this paper, we propose an improved method for community detection based on a scalable community “fitness function.” We introduce a new parameter to enhance its scalability, and a strict strategy to filter the outputs. Due to the scalability, on the one hand, our method is free of the resolution limit problem and performs excellently on large heterogeneous networks, while on the other hand, it is capable of detecting more levels of communities than previous methods in deep hierarchical networks. Moreover, our strict strategy automatically removes redundant and irrelevant results; it selectively but inartificially outputs only the best and unique community structures, which turn out to be largely interpretable by the a priori knowledge of the network, including the implanted community structures within synthetic networks, or metadata observed for real-world networks. |
| first_indexed | 2024-03-11T09:52:35Z |
| format | Article |
| id | doaj.art-f664666c069d41118fd2c6b0956f63a9 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T09:52:35Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-f664666c069d41118fd2c6b0956f63a92023-11-16T16:10:11ZengMDPI AGApplied Sciences2076-34172023-01-01133177410.3390/app13031774Automatic Detection of Multilevel Communities: Scalable, Selective and Resolution-Limit-FreeKun Gao0Xuezao Ren1Lei Zhou2Junfang Zhu3School of Mathematics and Physics, Southwest University of Science and Technology, Mianyang 621010, ChinaSchool of Mathematics and Physics, Southwest University of Science and Technology, Mianyang 621010, ChinaSchool of Mathematics and Physics, Southwest University of Science and Technology, Mianyang 621010, ChinaSchool of Mathematics and Physics, Southwest University of Science and Technology, Mianyang 621010, ChinaCommunity structure is one of the most important features of complex networks. Modularity-based methods for community detection typically rely on heuristic algorithms to optimize a specific community quality function. Such methods have two major limits: (1) the resolution limit problem, which prohibits communities of heterogeneous sizes being simultaneously detected, and (2) divergent outputs of the heuristic algorithm, which make it difficult to differentiate relevant and irrelevant results. In this paper, we propose an improved method for community detection based on a scalable community “fitness function.” We introduce a new parameter to enhance its scalability, and a strict strategy to filter the outputs. Due to the scalability, on the one hand, our method is free of the resolution limit problem and performs excellently on large heterogeneous networks, while on the other hand, it is capable of detecting more levels of communities than previous methods in deep hierarchical networks. Moreover, our strict strategy automatically removes redundant and irrelevant results; it selectively but inartificially outputs only the best and unique community structures, which turn out to be largely interpretable by the a priori knowledge of the network, including the implanted community structures within synthetic networks, or metadata observed for real-world networks.https://www.mdpi.com/2076-3417/13/3/1774community detectionresolution limit problemmodularitymultilevel communityLouvain algorithm |
| spellingShingle | Kun Gao Xuezao Ren Lei Zhou Junfang Zhu Automatic Detection of Multilevel Communities: Scalable, Selective and Resolution-Limit-Free Applied Sciences community detection resolution limit problem modularity multilevel community Louvain algorithm |
| title | Automatic Detection of Multilevel Communities: Scalable, Selective and Resolution-Limit-Free |
| title_full | Automatic Detection of Multilevel Communities: Scalable, Selective and Resolution-Limit-Free |
| title_fullStr | Automatic Detection of Multilevel Communities: Scalable, Selective and Resolution-Limit-Free |
| title_full_unstemmed | Automatic Detection of Multilevel Communities: Scalable, Selective and Resolution-Limit-Free |
| title_short | Automatic Detection of Multilevel Communities: Scalable, Selective and Resolution-Limit-Free |
| title_sort | automatic detection of multilevel communities scalable selective and resolution limit free |
| topic | community detection resolution limit problem modularity multilevel community Louvain algorithm |
| url | https://www.mdpi.com/2076-3417/13/3/1774 |
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