Research on the scalability of parallel community detection algorithms
The social network often contains a large amount of information about users and groups,such as topic evolution mode,group aggregation effect,the law of information dissemination and so on.The mining of these information has become an important task for social network analysis.As one characteristic o...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2018-04-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2018052/ |
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| _version_ | 1841539471502213120 |
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| author | Qiang LIU Yan JIA Binxing FANG Bin ZHOU Yue HU Jiuming HUANG |
| author_facet | Qiang LIU Yan JIA Binxing FANG Bin ZHOU Yue HU Jiuming HUANG |
| author_sort | Qiang LIU |
| collection | DOAJ |
| description | The social network often contains a large amount of information about users and groups,such as topic evolution mode,group aggregation effect,the law of information dissemination and so on.The mining of these information has become an important task for social network analysis.As one characteristic of the social network,the group aggregation effect is characterized by the community structure of the social network.The discovery of community structure has become the basis and key point of other social network analysis tasks.With the rapid growth of the number of online social network users,the traditional community detection methods have been difficult to be used,which contributes to the development of parallel community detection technology.The current mainstream parallel community detection methods,including Louvain algorithm and label propagation algorithm,were tested in the large-scale data sets,and corresponding advantages and disadvantages were pointed out so as to provide useful information for later applications. |
| format | Article |
| id | doaj-art-f9971113a57f4a0987d0b9953434311e |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2018-04-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-f9971113a57f4a0987d0b9953434311e2025-01-14T07:14:31ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2018-04-0139132059717422Research on the scalability of parallel community detection algorithmsQiang LIUYan JIABinxing FANGBin ZHOUYue HUJiuming HUANGThe social network often contains a large amount of information about users and groups,such as topic evolution mode,group aggregation effect,the law of information dissemination and so on.The mining of these information has become an important task for social network analysis.As one characteristic of the social network,the group aggregation effect is characterized by the community structure of the social network.The discovery of community structure has become the basis and key point of other social network analysis tasks.With the rapid growth of the number of online social network users,the traditional community detection methods have been difficult to be used,which contributes to the development of parallel community detection technology.The current mainstream parallel community detection methods,including Louvain algorithm and label propagation algorithm,were tested in the large-scale data sets,and corresponding advantages and disadvantages were pointed out so as to provide useful information for later applications.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2018052/community detectionparallel algorithmscalability |
| spellingShingle | Qiang LIU Yan JIA Binxing FANG Bin ZHOU Yue HU Jiuming HUANG Research on the scalability of parallel community detection algorithms Tongxin xuebao community detection parallel algorithm scalability |
| title | Research on the scalability of parallel community detection algorithms |
| title_full | Research on the scalability of parallel community detection algorithms |
| title_fullStr | Research on the scalability of parallel community detection algorithms |
| title_full_unstemmed | Research on the scalability of parallel community detection algorithms |
| title_short | Research on the scalability of parallel community detection algorithms |
| title_sort | research on the scalability of parallel community detection algorithms |
| topic | community detection parallel algorithm scalability |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2018052/ |
| work_keys_str_mv | AT qiangliu researchonthescalabilityofparallelcommunitydetectionalgorithms AT yanjia researchonthescalabilityofparallelcommunitydetectionalgorithms AT binxingfang researchonthescalabilityofparallelcommunitydetectionalgorithms AT binzhou researchonthescalabilityofparallelcommunitydetectionalgorithms AT yuehu researchonthescalabilityofparallelcommunitydetectionalgorithms AT jiuminghuang researchonthescalabilityofparallelcommunitydetectionalgorithms |