Improving Community Detection in Time-Evolving Networks Through Clustering Fusion
Traditional community detection algorithms are easily interfered by noises and outliers. Therefore, we propose to leverage a clustering fusion method to improve the results of community detection. Usually, there are two issues in clustering ensembles: how to generate efficient diversified cluster me...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Sciendo
2015-06-01
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| Series: | Cybernetics and Information Technologies |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/cait-2015-0029 |
| _version_ | 1811272547711320064 |
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| author | Jin Ran Kou Chunhai Liu Ruijuan |
| author_facet | Jin Ran Kou Chunhai Liu Ruijuan |
| author_sort | Jin Ran |
| collection | DOAJ |
| description | Traditional community detection algorithms are easily interfered by noises and outliers. Therefore, we propose to leverage a clustering fusion method to improve the results of community detection. Usually, there are two issues in clustering ensembles: how to generate efficient diversified cluster members, and how to ensembles the results of all members. Specifically: (1) considering the time evolving characteristic of real world networks, we propose to generate clustering members based on the snapshot of networks, where the split based clustering algorithms are performed; (2) considering the difference in the distribution of the cluster centers in each clustering member and the actual distribution, we ensemble the results based on a maximum likelihood method. Moreover, we conduct experiments to show that our method can discover high quality communities. |
| first_indexed | 2024-04-12T22:42:17Z |
| format | Article |
| id | doaj.art-1b1d02366d024e73927f75905817723b |
| institution | Directory Open Access Journal |
| issn | 1314-4081 |
| language | English |
| last_indexed | 2024-04-12T22:42:17Z |
| publishDate | 2015-06-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Cybernetics and Information Technologies |
| spelling | doaj.art-1b1d02366d024e73927f75905817723b2022-12-22T03:13:41ZengSciendoCybernetics and Information Technologies1314-40812015-06-01152637410.1515/cait-2015-0029Improving Community Detection in Time-Evolving Networks Through Clustering FusionJin Ran0Kou Chunhai1Liu Ruijuan2School of Information Science and Technology, Donghua University, Shanghai, 201620 ChinaSchool of Science, Donghua University, Shanghai, 201620 ChinaSchool of Information Science and Technology, Donghua University, Shanghai, 201620 ChinaTraditional community detection algorithms are easily interfered by noises and outliers. Therefore, we propose to leverage a clustering fusion method to improve the results of community detection. Usually, there are two issues in clustering ensembles: how to generate efficient diversified cluster members, and how to ensembles the results of all members. Specifically: (1) considering the time evolving characteristic of real world networks, we propose to generate clustering members based on the snapshot of networks, where the split based clustering algorithms are performed; (2) considering the difference in the distribution of the cluster centers in each clustering member and the actual distribution, we ensemble the results based on a maximum likelihood method. Moreover, we conduct experiments to show that our method can discover high quality communities.https://doi.org/10.1515/cait-2015-0029time-evolving networkcommunity detectionclustering fusionnetwork snapshotmaximum likelihood methodexpectation maximization algorithm. |
| spellingShingle | Jin Ran Kou Chunhai Liu Ruijuan Improving Community Detection in Time-Evolving Networks Through Clustering Fusion Cybernetics and Information Technologies time-evolving network community detection clustering fusion network snapshot maximum likelihood method expectation maximization algorithm. |
| title | Improving Community Detection in Time-Evolving Networks Through Clustering Fusion |
| title_full | Improving Community Detection in Time-Evolving Networks Through Clustering Fusion |
| title_fullStr | Improving Community Detection in Time-Evolving Networks Through Clustering Fusion |
| title_full_unstemmed | Improving Community Detection in Time-Evolving Networks Through Clustering Fusion |
| title_short | Improving Community Detection in Time-Evolving Networks Through Clustering Fusion |
| title_sort | improving community detection in time evolving networks through clustering fusion |
| topic | time-evolving network community detection clustering fusion network snapshot maximum likelihood method expectation maximization algorithm. |
| url | https://doi.org/10.1515/cait-2015-0029 |
| work_keys_str_mv | AT jinran improvingcommunitydetectionintimeevolvingnetworksthroughclusteringfusion AT kouchunhai improvingcommunitydetectionintimeevolvingnetworksthroughclusteringfusion AT liuruijuan improvingcommunitydetectionintimeevolvingnetworksthroughclusteringfusion |