A Consensus Community-Based Spider Wasp Optimization for Dynamic Community Detection
There are many evolving dynamic networks in the real world, and community detection in dynamic networks is crucial in many complex network analysis applications. In this paper, a consensus community-based discrete spider wasp optimization (SWO) approach is proposed for the dynamic network community...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/13/2/265 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832588073256353792 |
|---|---|
| author | Lin Yu Xin Zhao Ming Lv Jie Zhang |
| author_facet | Lin Yu Xin Zhao Ming Lv Jie Zhang |
| author_sort | Lin Yu |
| collection | DOAJ |
| description | There are many evolving dynamic networks in the real world, and community detection in dynamic networks is crucial in many complex network analysis applications. In this paper, a consensus community-based discrete spider wasp optimization (SWO) approach is proposed for the dynamic network community detection problem. First, the coding, initialization, and updating strategies of the spider wasp optimization algorithm are discretized to adapt to the community detection problem. Second, the concept of intra-population and inter-population consensus community is proposed. Consensus community is the knowledge formed by the swarm summarizing the current state as well as the past history. By maintaining certain inter-population consensus community during the evolutionary process, the population in the current time window can evolve in a similar direction to those in the previous time step. Experimental results on many artificial and real dynamic networks show that the proposed method produces more accurate and robust results than current methods. |
| format | Article |
| id | doaj-art-8e366abfb2d0423faa10711102022c3f |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-8e366abfb2d0423faa10711102022c3f2025-01-24T13:39:57ZengMDPI AGMathematics2227-73902025-01-0113226510.3390/math13020265A Consensus Community-Based Spider Wasp Optimization for Dynamic Community DetectionLin Yu0Xin Zhao1Ming Lv2Jie Zhang3School of Automation, Nanjing University of Science and Technology, Xiaolingwei Street, Nanjing 210094, ChinaNational Key Laboratory of Information Systems Engineering, Nanjing Research Institute of Electronic Engineering, Huitong Street, Nanjing 210007, ChinaSchool of Automation, Nanjing University of Science and Technology, Xiaolingwei Street, Nanjing 210094, ChinaSchool of Automation, Nanjing University of Science and Technology, Xiaolingwei Street, Nanjing 210094, ChinaThere are many evolving dynamic networks in the real world, and community detection in dynamic networks is crucial in many complex network analysis applications. In this paper, a consensus community-based discrete spider wasp optimization (SWO) approach is proposed for the dynamic network community detection problem. First, the coding, initialization, and updating strategies of the spider wasp optimization algorithm are discretized to adapt to the community detection problem. Second, the concept of intra-population and inter-population consensus community is proposed. Consensus community is the knowledge formed by the swarm summarizing the current state as well as the past history. By maintaining certain inter-population consensus community during the evolutionary process, the population in the current time window can evolve in a similar direction to those in the previous time step. Experimental results on many artificial and real dynamic networks show that the proposed method produces more accurate and robust results than current methods.https://www.mdpi.com/2227-7390/13/2/265complex networkscommunity detectionheuristic algorithmspider wasp optimizationconsensus communitymulti-objective optimization |
| spellingShingle | Lin Yu Xin Zhao Ming Lv Jie Zhang A Consensus Community-Based Spider Wasp Optimization for Dynamic Community Detection Mathematics complex networks community detection heuristic algorithm spider wasp optimization consensus community multi-objective optimization |
| title | A Consensus Community-Based Spider Wasp Optimization for Dynamic Community Detection |
| title_full | A Consensus Community-Based Spider Wasp Optimization for Dynamic Community Detection |
| title_fullStr | A Consensus Community-Based Spider Wasp Optimization for Dynamic Community Detection |
| title_full_unstemmed | A Consensus Community-Based Spider Wasp Optimization for Dynamic Community Detection |
| title_short | A Consensus Community-Based Spider Wasp Optimization for Dynamic Community Detection |
| title_sort | consensus community based spider wasp optimization for dynamic community detection |
| topic | complex networks community detection heuristic algorithm spider wasp optimization consensus community multi-objective optimization |
| url | https://www.mdpi.com/2227-7390/13/2/265 |
| work_keys_str_mv | AT linyu aconsensuscommunitybasedspiderwaspoptimizationfordynamiccommunitydetection AT xinzhao aconsensuscommunitybasedspiderwaspoptimizationfordynamiccommunitydetection AT minglv aconsensuscommunitybasedspiderwaspoptimizationfordynamiccommunitydetection AT jiezhang aconsensuscommunitybasedspiderwaspoptimizationfordynamiccommunitydetection AT linyu consensuscommunitybasedspiderwaspoptimizationfordynamiccommunitydetection AT xinzhao consensuscommunitybasedspiderwaspoptimizationfordynamiccommunitydetection AT minglv consensuscommunitybasedspiderwaspoptimizationfordynamiccommunitydetection AT jiezhang consensuscommunitybasedspiderwaspoptimizationfordynamiccommunitydetection |