Unsupervised Community Detection Algorithm with Stochastic Competitive Learning Incorporating Local Node Similarity
Community detection is an important task in the analysis of complex networks, which is significant for mining and analyzing the organization and function of networks. As an unsupervised learning algorithm based on the particle competition mechanism, stochastic competitive learning has been applied i...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/18/10496 |
| _version_ | 1827727318203760640 |
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| author | Jian Huang Yijun Gu |
| author_facet | Jian Huang Yijun Gu |
| author_sort | Jian Huang |
| collection | DOAJ |
| description | Community detection is an important task in the analysis of complex networks, which is significant for mining and analyzing the organization and function of networks. As an unsupervised learning algorithm based on the particle competition mechanism, stochastic competitive learning has been applied in the field of community detection in complex networks, but still has several limitations. In order to improve the stability and accuracy of stochastic competitive learning and solve the problem of community detection, we propose an unsupervised community detection algorithm LNSSCL (Local Node Similarity-Integrated Stochastic Competitive Learning). The algorithm calculates node degree as well as Salton similarity metrics to determine the starting position of particle walk; local node similarity is incorporated into the particle preferential walk rule; the particle is dynamically adjusted to control capability increments according to the control range; particles select the node with the strongest control capability within the node to be resurrected; and the LNSSCL algorithm introduces a node affiliation selection step to adjust the node community labels. Experimental comparisons with 12 representative community detection algorithms on real network datasets and synthetic networks show that the LNSSCL algorithm is overall better than other compared algorithms in terms of standardized mutual information (NMI) and modularity (Q). The improvement effect for the stochastic competition learning algorithm is evident, and it can effectively accomplish the community detection task in complex networks. |
| first_indexed | 2024-03-10T23:03:48Z |
| format | Article |
| id | doaj.art-b002d3abd05b4d6592fcd19f0c2513b0 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T23:03:48Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-b002d3abd05b4d6592fcd19f0c2513b02023-11-19T09:28:34ZengMDPI AGApplied Sciences2076-34172023-09-0113181049610.3390/app131810496Unsupervised Community Detection Algorithm with Stochastic Competitive Learning Incorporating Local Node SimilarityJian Huang0Yijun Gu1College of Information and Cyber Security, People’s Public Security University of China, Beijing 100038, ChinaCollege of Information and Cyber Security, People’s Public Security University of China, Beijing 100038, ChinaCommunity detection is an important task in the analysis of complex networks, which is significant for mining and analyzing the organization and function of networks. As an unsupervised learning algorithm based on the particle competition mechanism, stochastic competitive learning has been applied in the field of community detection in complex networks, but still has several limitations. In order to improve the stability and accuracy of stochastic competitive learning and solve the problem of community detection, we propose an unsupervised community detection algorithm LNSSCL (Local Node Similarity-Integrated Stochastic Competitive Learning). The algorithm calculates node degree as well as Salton similarity metrics to determine the starting position of particle walk; local node similarity is incorporated into the particle preferential walk rule; the particle is dynamically adjusted to control capability increments according to the control range; particles select the node with the strongest control capability within the node to be resurrected; and the LNSSCL algorithm introduces a node affiliation selection step to adjust the node community labels. Experimental comparisons with 12 representative community detection algorithms on real network datasets and synthetic networks show that the LNSSCL algorithm is overall better than other compared algorithms in terms of standardized mutual information (NMI) and modularity (Q). The improvement effect for the stochastic competition learning algorithm is evident, and it can effectively accomplish the community detection task in complex networks.https://www.mdpi.com/2076-3417/13/18/10496unsupervised learningcommunity detectionlocal node similarityparticle competitionstochastic competitive learningcomplex networks |
| spellingShingle | Jian Huang Yijun Gu Unsupervised Community Detection Algorithm with Stochastic Competitive Learning Incorporating Local Node Similarity Applied Sciences unsupervised learning community detection local node similarity particle competition stochastic competitive learning complex networks |
| title | Unsupervised Community Detection Algorithm with Stochastic Competitive Learning Incorporating Local Node Similarity |
| title_full | Unsupervised Community Detection Algorithm with Stochastic Competitive Learning Incorporating Local Node Similarity |
| title_fullStr | Unsupervised Community Detection Algorithm with Stochastic Competitive Learning Incorporating Local Node Similarity |
| title_full_unstemmed | Unsupervised Community Detection Algorithm with Stochastic Competitive Learning Incorporating Local Node Similarity |
| title_short | Unsupervised Community Detection Algorithm with Stochastic Competitive Learning Incorporating Local Node Similarity |
| title_sort | unsupervised community detection algorithm with stochastic competitive learning incorporating local node similarity |
| topic | unsupervised learning community detection local node similarity particle competition stochastic competitive learning complex networks |
| url | https://www.mdpi.com/2076-3417/13/18/10496 |
| work_keys_str_mv | AT jianhuang unsupervisedcommunitydetectionalgorithmwithstochasticcompetitivelearningincorporatinglocalnodesimilarity AT yijungu unsupervisedcommunitydetectionalgorithmwithstochasticcompetitivelearningincorporatinglocalnodesimilarity |