Residential College | false |
Status | 已發表Published |
Evaluating Edge Credibility in Evolving Noisy Social Networks | |
Wang, Huan1,4,5; Liu, Shun2; Ni, Qiufen3; Gong, Zhiguo4,6 | |
2022 | |
Source Publication | IEEE Transactions on Knowledge and Data Engineering |
ISSN | 1041-4347 |
Volume | 35Issue:11Pages:11342 - 11353 |
Abstract | Despite the massive surge of evolving social network analysis in popularity, existing research usually represent the observed social interactions among individuals as completely credible edges. However, due to information inaccuracy, individual non-response and dropout, and sampling biases in observations, the evolving noisy social network that coexists true edges and spurious edges is pervasive in actual applications, where the ignoration of credibility otherness of observed edges could lead to the wrong estimates of social properties and misleading conclusions. To discover credible edge information to shape correct social interactions among individuals, we propose a universal and explainable multiple-neighbor evolutional filtering method (MEFM) to evaluate how credible of observed edges to ‘truly’ exist in the evolving noisy social network. MEFM consists of an evolutional extractor and a filtering evaluator. To resist the noisy disturbance, the evolutional extractor exploits the evolutional states of edges from the perspective of evolution mechanisms within multiple-neighbor ranges, which applies different link prediction algorithms to fit the evolution mechanism in the formation of each edge. Further, the filtering evaluator reconstructs Kalman filter to predict and refine the evolutional states of edges based on their evolving local structures. As a result, MEFM combines the evolutional extractor and the filtering evaluator to analyze the evolutional fluctuations of the observed edges to evaluate their credibility. Extensive experiments on real-world datasets demonstrate that our proposed MEFM can effectively and reasonably evaluate edge credibility in evolving noisy social networks. |
Keyword | Evolving Noisy Social Network Evolution Mechanism Edge Credibility Multiple-neighbor Ranges |
DOI | 10.1109/TKDE.2022.3223403 |
URL | View the original |
Indexed By | SCIE |
Language | 英語English |
WOS Research Area | Computer Science ; Engineering |
WOS Subject | Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic |
WOS ID | WOS:001089176900030 |
Publisher | IEEE Computer Society |
Scopus ID | 2-s2.0-85144089415 |
Fulltext Access | |
Citation statistics | |
Document Type | Journal article |
Collection | Faculty of Science and Technology THE STATE KEY LABORATORY OF INTERNET OF THINGS FOR SMART CITY (UNIVERSITY OF MACAU) DEPARTMENT OF ELECTRICAL AND COMPUTER ENGINEERING DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE |
Corresponding Author | Ni, Qiufen |
Affiliation | 1.College of Informatics, Huazhong Agricultural University, China 2.Department of Electrical and Computer Engineering, University of Macau, China 3.School of Computers, Guangdong University of Technology, Guangzhou, China 4.State Key Laboratory of Internet of Things for Smart City, University of Macau, Macau, China 5.Key Laboratory of Smart Farming for Agricultural Animals, Engineering Research Center of Intelligent Technology for Agriculture, Ministry of Education, Zhengzhou Xinda Institute of Advanced Technology, China 6.Department of Computer Information Science, University of Macau, Macau, China |
First Author Affilication | University of Macau |
Recommended Citation GB/T 7714 | Wang, Huan,Liu, Shun,Ni, Qiufen,et al. Evaluating Edge Credibility in Evolving Noisy Social Networks[J]. IEEE Transactions on Knowledge and Data Engineering, 2022, 35(11), 11342 - 11353. |
APA | Wang, Huan., Liu, Shun., Ni, Qiufen., & Gong, Zhiguo (2022). Evaluating Edge Credibility in Evolving Noisy Social Networks. IEEE Transactions on Knowledge and Data Engineering, 35(11), 11342 - 11353. |
MLA | Wang, Huan,et al."Evaluating Edge Credibility in Evolving Noisy Social Networks".IEEE Transactions on Knowledge and Data Engineering 35.11(2022):11342 - 11353. |
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