Social Network Community Detection Using Agglomerative Spectral Clustering
Community detection has become an increasingly popular tool for analyzing and researching complex networks. Many methods have been proposed for accurate community detection, and one of them is spectral clustering. Most spectral clustering algorithms have been implemented on artificial networks, and...
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2017/3719428 |
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| _version_ | 1832566571683282944 |
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| author | Ulzii-Utas Narantsatsralt Sanggil Kang |
| author_facet | Ulzii-Utas Narantsatsralt Sanggil Kang |
| author_sort | Ulzii-Utas Narantsatsralt |
| collection | DOAJ |
| description | Community detection has become an increasingly popular tool for analyzing and researching complex networks. Many methods have been proposed for accurate community detection, and one of them is spectral clustering. Most spectral clustering algorithms have been implemented on artificial networks, and accuracy of the community detection is still unsatisfactory. Therefore, this paper proposes an agglomerative spectral clustering method with conductance and edge weights. In this method, the most similar nodes are agglomerated based on eigenvector space and edge weights. In addition, the conductance is used to identify densely connected clusters while agglomerating. The proposed method shows improved performance in related works and proves to be efficient for real life complex networks from experiments. |
| format | Article |
| id | doaj-art-c76bc96325e44e12b87ca6a1c4dedb98 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-c76bc96325e44e12b87ca6a1c4dedb982025-02-03T01:03:49ZengWileyComplexity1076-27871099-05262017-01-01201710.1155/2017/37194283719428Social Network Community Detection Using Agglomerative Spectral ClusteringUlzii-Utas Narantsatsralt0Sanggil Kang1Department of Computer Engineering, Inha University, Incheon, Republic of KoreaDepartment of Computer Engineering, Inha University, Incheon, Republic of KoreaCommunity detection has become an increasingly popular tool for analyzing and researching complex networks. Many methods have been proposed for accurate community detection, and one of them is spectral clustering. Most spectral clustering algorithms have been implemented on artificial networks, and accuracy of the community detection is still unsatisfactory. Therefore, this paper proposes an agglomerative spectral clustering method with conductance and edge weights. In this method, the most similar nodes are agglomerated based on eigenvector space and edge weights. In addition, the conductance is used to identify densely connected clusters while agglomerating. The proposed method shows improved performance in related works and proves to be efficient for real life complex networks from experiments.http://dx.doi.org/10.1155/2017/3719428 |
| spellingShingle | Ulzii-Utas Narantsatsralt Sanggil Kang Social Network Community Detection Using Agglomerative Spectral Clustering Complexity |
| title | Social Network Community Detection Using Agglomerative Spectral Clustering |
| title_full | Social Network Community Detection Using Agglomerative Spectral Clustering |
| title_fullStr | Social Network Community Detection Using Agglomerative Spectral Clustering |
| title_full_unstemmed | Social Network Community Detection Using Agglomerative Spectral Clustering |
| title_short | Social Network Community Detection Using Agglomerative Spectral Clustering |
| title_sort | social network community detection using agglomerative spectral clustering |
| url | http://dx.doi.org/10.1155/2017/3719428 |
| work_keys_str_mv | AT ulziiutasnarantsatsralt socialnetworkcommunitydetectionusingagglomerativespectralclustering AT sanggilkang socialnetworkcommunitydetectionusingagglomerativespectralclustering |