Journal article
Managing Uncertainty in Web-Based Social Networks
International journal of uncertainty, fuzziness, and knowledge-based systems, v 20(supp01), pp 147-158
Jun 2012
Abstract
Identifying key members from web-based social networks assists in assessing the risk of criminal network formation. To manage the uncertainty in complex web-based social networks, we first formally defined the binary relation and uncertainty of pages in web-based social networks. Secondly, we proposed an effective algorithm for Mining Key member from uncertain web-based social networks, called MiKey, by integrating uncertainty of pages into three centrality measures including degree, betweenness, and closeness. MiKey takes into a full consideration of the uncertainty in web-based social networks by computing the transition probability from one page to another. Furthermore, we briefly introduced the approach of calculating the k-order transition matrix of pages. Finally, we conducted experiments on real web data and the results show that MiKey is effective in discovering key pages from web-based social networks with less time deficiency than the centrality measures based algorithm.
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Details
- Title
- Managing Uncertainty in Web-Based Social Networks
- Creators
- SHAOJIE QIAO - School of Information Science and Technology, Southwest Jiaotong University, No. 111, Erhuanlu Beiyiduan, Chengdu, Sichuan 610031, ChinaTIANRUI LI - School of Information Science and Technology, Southwest Jiaotong University, No. 111, Erhuanlu Beiyiduan, Chengdu, Sichuan 610031, ChinaYAN YANG - School of Information Science and Technology, Southwest Jiaotong University, No. 111, Erhuanlu Beiyiduan, Chengdu, Sichuan 610031, ChinaCHRISTOPHER C YANG - College of Information Science and Technology, Drexel University, 3141 Chestnut Street, Philadelphia, Pennsylvania 19104, USA
- Publication Details
- International journal of uncertainty, fuzziness, and knowledge-based systems, v 20(supp01), pp 147-158
- Publisher
- World Scientific Publishing
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000306184900013
- Scopus ID
- 2-s2.0-84863735392
- Other Identifier
- 991014877910304721
InCites Highlights
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence