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Evaluating Edge Credibility in Evolving Noisy Social Networks
Wang, Huan1,4,5; Liu, Shun2; Ni, Qiufen3; Gong, Zhiguo4,6
2022
Source PublicationIEEE Transactions on Knowledge and Data Engineering
ISSN1041-4347
Volume35Issue: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.

KeywordEvolving Noisy Social Network Evolution Mechanism Edge Credibility Multiple-neighbor Ranges
DOI10.1109/TKDE.2022.3223403
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic
WOS IDWOS:001089176900030
PublisherIEEE Computer Society
Scopus ID2-s2.0-85144089415
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Citation statistics
Document TypeJournal article
CollectionFaculty 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 AuthorNi, Qiufen
Affiliation1.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 AffilicationUniversity 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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