A Local Extended Algorithm Combined with Degree and Clustering Coefficient to Optimize Overlapping Community Detection
Community structure is one of the most important characteristics of complex networks, which has important applications in sociology, biology, and computer science. The community detection method based on local expansion is one of the most adaptable overlapping community detection algorithms. However...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/7428927 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832566446895398912 |
|---|---|
| author | Jing Liu Junfang Guo Qi Li |
| author_facet | Jing Liu Junfang Guo Qi Li |
| author_sort | Jing Liu |
| collection | DOAJ |
| description | Community structure is one of the most important characteristics of complex networks, which has important applications in sociology, biology, and computer science. The community detection method based on local expansion is one of the most adaptable overlapping community detection algorithms. However, due to the lack of effective seed selection and community optimization methods, the algorithm often gets community results with lower accuracy. In order to solve these problems, we propose a seed selection algorithm of fusion degree and clustering coefficient. The method calculates the weight value corresponding to degree and clustering coefficient by entropy weight method and then calculates the weight factor of nodes as the seed node selection order. Based on the seed selection algorithm, we design a local expansion strategy, which uses the strategy of optimizing adaptive function to expand the community. Finally, community merging and isolated node adjustment strategies are adopted to obtain the final community. Experimental results show that the proposed algorithm can achieve better community partitioning results than other state-of-the-art algorithms. |
| format | Article |
| id | doaj-art-ed96227ac08942bbb29ec28a7b3d8542 |
| institution | Kabale University |
| issn | 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-ed96227ac08942bbb29ec28a7b3d85422025-02-03T01:04:16ZengWileyComplexity1099-05262021-01-01202110.1155/2021/7428927A Local Extended Algorithm Combined with Degree and Clustering Coefficient to Optimize Overlapping Community DetectionJing Liu0Junfang Guo1Qi Li2Department of Computer Science and EngineeringDepartment of Computer Science and EngineeringDepartment of Computer Science and EngineeringCommunity structure is one of the most important characteristics of complex networks, which has important applications in sociology, biology, and computer science. The community detection method based on local expansion is one of the most adaptable overlapping community detection algorithms. However, due to the lack of effective seed selection and community optimization methods, the algorithm often gets community results with lower accuracy. In order to solve these problems, we propose a seed selection algorithm of fusion degree and clustering coefficient. The method calculates the weight value corresponding to degree and clustering coefficient by entropy weight method and then calculates the weight factor of nodes as the seed node selection order. Based on the seed selection algorithm, we design a local expansion strategy, which uses the strategy of optimizing adaptive function to expand the community. Finally, community merging and isolated node adjustment strategies are adopted to obtain the final community. Experimental results show that the proposed algorithm can achieve better community partitioning results than other state-of-the-art algorithms.http://dx.doi.org/10.1155/2021/7428927 |
| spellingShingle | Jing Liu Junfang Guo Qi Li A Local Extended Algorithm Combined with Degree and Clustering Coefficient to Optimize Overlapping Community Detection Complexity |
| title | A Local Extended Algorithm Combined with Degree and Clustering Coefficient to Optimize Overlapping Community Detection |
| title_full | A Local Extended Algorithm Combined with Degree and Clustering Coefficient to Optimize Overlapping Community Detection |
| title_fullStr | A Local Extended Algorithm Combined with Degree and Clustering Coefficient to Optimize Overlapping Community Detection |
| title_full_unstemmed | A Local Extended Algorithm Combined with Degree and Clustering Coefficient to Optimize Overlapping Community Detection |
| title_short | A Local Extended Algorithm Combined with Degree and Clustering Coefficient to Optimize Overlapping Community Detection |
| title_sort | local extended algorithm combined with degree and clustering coefficient to optimize overlapping community detection |
| url | http://dx.doi.org/10.1155/2021/7428927 |
| work_keys_str_mv | AT jingliu alocalextendedalgorithmcombinedwithdegreeandclusteringcoefficienttooptimizeoverlappingcommunitydetection AT junfangguo alocalextendedalgorithmcombinedwithdegreeandclusteringcoefficienttooptimizeoverlappingcommunitydetection AT qili alocalextendedalgorithmcombinedwithdegreeandclusteringcoefficienttooptimizeoverlappingcommunitydetection AT jingliu localextendedalgorithmcombinedwithdegreeandclusteringcoefficienttooptimizeoverlappingcommunitydetection AT junfangguo localextendedalgorithmcombinedwithdegreeandclusteringcoefficienttooptimizeoverlappingcommunitydetection AT qili localextendedalgorithmcombinedwithdegreeandclusteringcoefficienttooptimizeoverlappingcommunitydetection |