Mining Community-Level Influence in Microblogging Network: A Case Study on Sina Weibo
Social influence analysis is important for many social network applications, including recommendation and cybersecurity analysis. We observe that the influence of community including multiple users outweighs the individual influence. Existing models focus on the individual influence analysis, but fe...
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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/10.1155/2017/4783159 |
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| author | Yufei Liu Dechang Pi Lin Cui |
| author_facet | Yufei Liu Dechang Pi Lin Cui |
| author_sort | Yufei Liu |
| collection | DOAJ |
| description | Social influence analysis is important for many social network applications, including recommendation and cybersecurity analysis. We observe that the influence of community including multiple users outweighs the individual influence. Existing models focus on the individual influence analysis, but few studies estimate the community influence that is ubiquitous in online social network. A major challenge lies in that researchers need to take into account many factors, such as user influence, social trust, and user relationship, to model community-level influence. In this paper, aiming to assess the community-level influence effectively and accurately, we formulate the problem of modeling community influence and construct a community-level influence analysis model. It first eliminates the zombie fans and then calculates the user influence. Next, it calculates the user final influence by combining the user influence and the willingness of diffusing theme information. Finally, it evaluates the community influence by comprehensively studying the user final influence, social trust, and relationship tightness between intrausers of communities. To handle real-world applications, we propose a community-level influence analysis algorithm called CIAA. Empirical studies on a real-world dataset from Sina Weibo demonstrate the superiority of the proposed model. |
| format | Article |
| id | doaj-art-0a40638ec9f349668b75752155ba5e25 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-0a40638ec9f349668b75752155ba5e252025-02-03T01:12:37ZengWileyComplexity1076-27871099-05262017-01-01201710.1155/2017/47831594783159Mining Community-Level Influence in Microblogging Network: A Case Study on Sina WeiboYufei Liu0Dechang Pi1Lin Cui2College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu 211106, ChinaCollege of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu 211106, ChinaCollege of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu 211106, ChinaSocial influence analysis is important for many social network applications, including recommendation and cybersecurity analysis. We observe that the influence of community including multiple users outweighs the individual influence. Existing models focus on the individual influence analysis, but few studies estimate the community influence that is ubiquitous in online social network. A major challenge lies in that researchers need to take into account many factors, such as user influence, social trust, and user relationship, to model community-level influence. In this paper, aiming to assess the community-level influence effectively and accurately, we formulate the problem of modeling community influence and construct a community-level influence analysis model. It first eliminates the zombie fans and then calculates the user influence. Next, it calculates the user final influence by combining the user influence and the willingness of diffusing theme information. Finally, it evaluates the community influence by comprehensively studying the user final influence, social trust, and relationship tightness between intrausers of communities. To handle real-world applications, we propose a community-level influence analysis algorithm called CIAA. Empirical studies on a real-world dataset from Sina Weibo demonstrate the superiority of the proposed model.http://dx.doi.org/10.1155/2017/4783159 |
| spellingShingle | Yufei Liu Dechang Pi Lin Cui Mining Community-Level Influence in Microblogging Network: A Case Study on Sina Weibo Complexity |
| title | Mining Community-Level Influence in Microblogging Network: A Case Study on Sina Weibo |
| title_full | Mining Community-Level Influence in Microblogging Network: A Case Study on Sina Weibo |
| title_fullStr | Mining Community-Level Influence in Microblogging Network: A Case Study on Sina Weibo |
| title_full_unstemmed | Mining Community-Level Influence in Microblogging Network: A Case Study on Sina Weibo |
| title_short | Mining Community-Level Influence in Microblogging Network: A Case Study on Sina Weibo |
| title_sort | mining community level influence in microblogging network a case study on sina weibo |
| url | http://dx.doi.org/10.1155/2017/4783159 |
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