TNS-LPA: An Improved Label Propagation Algorithm for Community Detection Based on Two-Level Neighbourhood Similarity
Community detection can not only help people understand organizational structure and function of complex networks, but also attributes to many potential applications including targeted advertising and customer relationship management. Due to the low time complexity, the label propagation algorithm i...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9294111/ |
| _version_ | 1818932206462042112 |
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| author | Guiqiong Xu Jiawen Guo Pingle Yang |
| author_facet | Guiqiong Xu Jiawen Guo Pingle Yang |
| author_sort | Guiqiong Xu |
| collection | DOAJ |
| description | Community detection can not only help people understand organizational structure and function of complex networks, but also attributes to many potential applications including targeted advertising and customer relationship management. Due to the low time complexity, the label propagation algorithm is widely used, but there is still room to improve the community quality and the detection stability. Inspired by resource allocation and local path similarity, we first give a new two-level neighbourhood similarity measure called TNS, and on this basis we propose an improved label propagation algorithm for community detection. In this new algorithm, the minimum distance and local centrality index are considered to select the initial community centers, to ensure that they are both important and far away from each other. In the process of forming initial community, we employ the new similarity measure and an optimization strategy of asynchronously updating labels according to node importance. To further improve the accuracy of community division, we introduce the label influence based on the new similarity measure to further optimize the community division of networks. The experimental results on both the artificial network and ten real-world networks show that our proposed algorithm has better comprehensive performance than several existing algorithms in terms of modularity, normalized mutual information and adjusted rand index. |
| first_indexed | 2024-12-20T04:28:48Z |
| format | Article |
| id | doaj.art-172a39a10a734ab99db35a304793731f |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T04:28:48Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-172a39a10a734ab99db35a304793731f2022-12-21T19:53:26ZengIEEEIEEE Access2169-35362021-01-019235262353610.1109/ACCESS.2020.30450859294111TNS-LPA: An Improved Label Propagation Algorithm for Community Detection Based on Two-Level Neighbourhood SimilarityGuiqiong Xu0Jiawen Guo1Pingle Yang2https://orcid.org/0000-0002-6233-5557Department of Information Management, School of Management, Shanghai University, Shanghai, ChinaDepartment of Information Management, School of Management, Shanghai University, Shanghai, ChinaBusiness School, University of Shanghai for Science and Technology, Shanghai, ChinaCommunity detection can not only help people understand organizational structure and function of complex networks, but also attributes to many potential applications including targeted advertising and customer relationship management. Due to the low time complexity, the label propagation algorithm is widely used, but there is still room to improve the community quality and the detection stability. Inspired by resource allocation and local path similarity, we first give a new two-level neighbourhood similarity measure called TNS, and on this basis we propose an improved label propagation algorithm for community detection. In this new algorithm, the minimum distance and local centrality index are considered to select the initial community centers, to ensure that they are both important and far away from each other. In the process of forming initial community, we employ the new similarity measure and an optimization strategy of asynchronously updating labels according to node importance. To further improve the accuracy of community division, we introduce the label influence based on the new similarity measure to further optimize the community division of networks. The experimental results on both the artificial network and ten real-world networks show that our proposed algorithm has better comprehensive performance than several existing algorithms in terms of modularity, normalized mutual information and adjusted rand index.https://ieeexplore.ieee.org/document/9294111/Complex networkscommunity detectionlabel propagationcommunity kernellocal similarity measure |
| spellingShingle | Guiqiong Xu Jiawen Guo Pingle Yang TNS-LPA: An Improved Label Propagation Algorithm for Community Detection Based on Two-Level Neighbourhood Similarity IEEE Access Complex networks community detection label propagation community kernel local similarity measure |
| title | TNS-LPA: An Improved Label Propagation Algorithm for Community Detection Based on Two-Level Neighbourhood Similarity |
| title_full | TNS-LPA: An Improved Label Propagation Algorithm for Community Detection Based on Two-Level Neighbourhood Similarity |
| title_fullStr | TNS-LPA: An Improved Label Propagation Algorithm for Community Detection Based on Two-Level Neighbourhood Similarity |
| title_full_unstemmed | TNS-LPA: An Improved Label Propagation Algorithm for Community Detection Based on Two-Level Neighbourhood Similarity |
| title_short | TNS-LPA: An Improved Label Propagation Algorithm for Community Detection Based on Two-Level Neighbourhood Similarity |
| title_sort | tns lpa an improved label propagation algorithm for community detection based on two level neighbourhood similarity |
| topic | Complex networks community detection label propagation community kernel local similarity measure |
| url | https://ieeexplore.ieee.org/document/9294111/ |
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