Research on Dynamic Community Detection Method Based on an Improved Pity Beetle Algorithm
In the last decade, community detection in dynamic networks has received increasing attention, because it can not only uncover the community structure of the network at any time but also reveal the regularity of dynamic networks evolution. Although methods based on the framework of evolutionary clus...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9759363/ |
| _version_ | 1828284209646534656 |
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| author | Yan-Jiao Wang Jia-Xu Song Peng Sun |
| author_facet | Yan-Jiao Wang Jia-Xu Song Peng Sun |
| author_sort | Yan-Jiao Wang |
| collection | DOAJ |
| description | In the last decade, community detection in dynamic networks has received increasing attention, because it can not only uncover the community structure of the network at any time but also reveal the regularity of dynamic networks evolution. Although methods based on the framework of evolutionary clustering are promising for dynamic community detection, there is still room for further improvement in the snapshot quality and the temporal cost. In this study, a dynamic community detection algorithm based on optional pathway guide pity beetle algorithm (DYN-OPGPBA), which is a novel dynamic community detection method based on the framework of evolutionary clustering, is proposed. We propose an improved PBA for community detection of the network at the first time step, including a discrete search strategy based on adjacent nodes, a closeness-based community modification strategy and a crowded community split strategy. Compared with many representative static community detection methods, the proposed method has some superior detection accuracy. A neighbour vector competition-based individual update strategy and an external population size restriction mechanism are also proposed for community detection at subsequent time steps. Results show that DYN-OPGPBA has a better balance between snapshot quality and temporal cost than two representative dynamic community detection methods. |
| first_indexed | 2024-04-13T08:57:01Z |
| format | Article |
| id | doaj.art-d25907c1eec344f19f4e4002a74b3272 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-13T08:57:01Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-d25907c1eec344f19f4e4002a74b32722022-12-22T02:53:15ZengIEEEIEEE Access2169-35362022-01-0110439144393310.1109/ACCESS.2022.31687149759363Research on Dynamic Community Detection Method Based on an Improved Pity Beetle AlgorithmYan-Jiao Wang0https://orcid.org/0000-0003-3333-7438Jia-Xu Song1https://orcid.org/0000-0002-2757-0666Peng Sun2https://orcid.org/0000-0002-8643-9238School of Electrical Engineering, Northeast Electric Power University, Jilin, ChinaSchool of Electrical Engineering, Northeast Electric Power University, Jilin, ChinaSchool of Electrical Engineering, Northeast Electric Power University, Jilin, ChinaIn the last decade, community detection in dynamic networks has received increasing attention, because it can not only uncover the community structure of the network at any time but also reveal the regularity of dynamic networks evolution. Although methods based on the framework of evolutionary clustering are promising for dynamic community detection, there is still room for further improvement in the snapshot quality and the temporal cost. In this study, a dynamic community detection algorithm based on optional pathway guide pity beetle algorithm (DYN-OPGPBA), which is a novel dynamic community detection method based on the framework of evolutionary clustering, is proposed. We propose an improved PBA for community detection of the network at the first time step, including a discrete search strategy based on adjacent nodes, a closeness-based community modification strategy and a crowded community split strategy. Compared with many representative static community detection methods, the proposed method has some superior detection accuracy. A neighbour vector competition-based individual update strategy and an external population size restriction mechanism are also proposed for community detection at subsequent time steps. Results show that DYN-OPGPBA has a better balance between snapshot quality and temporal cost than two representative dynamic community detection methods.https://ieeexplore.ieee.org/document/9759363/Community detectiondynamic networkevolutionary clusteringPity Beetle algorithm |
| spellingShingle | Yan-Jiao Wang Jia-Xu Song Peng Sun Research on Dynamic Community Detection Method Based on an Improved Pity Beetle Algorithm IEEE Access Community detection dynamic network evolutionary clustering Pity Beetle algorithm |
| title | Research on Dynamic Community Detection Method Based on an Improved Pity Beetle Algorithm |
| title_full | Research on Dynamic Community Detection Method Based on an Improved Pity Beetle Algorithm |
| title_fullStr | Research on Dynamic Community Detection Method Based on an Improved Pity Beetle Algorithm |
| title_full_unstemmed | Research on Dynamic Community Detection Method Based on an Improved Pity Beetle Algorithm |
| title_short | Research on Dynamic Community Detection Method Based on an Improved Pity Beetle Algorithm |
| title_sort | research on dynamic community detection method based on an improved pity beetle algorithm |
| topic | Community detection dynamic network evolutionary clustering Pity Beetle algorithm |
| url | https://ieeexplore.ieee.org/document/9759363/ |
| work_keys_str_mv | AT yanjiaowang researchondynamiccommunitydetectionmethodbasedonanimprovedpitybeetlealgorithm AT jiaxusong researchondynamiccommunitydetectionmethodbasedonanimprovedpitybeetlealgorithm AT pengsun researchondynamiccommunitydetectionmethodbasedonanimprovedpitybeetlealgorithm |