Measuring influence in online social network based on the user-content bipartite graph.
With the rising of online social networks, influence has been a complex and subtle force to govern users’ behaviors and relationship formation. Therefore, how to precisely identify and measure influence has been a hot research direction. Differentiating from existing researches, we are devoted to co...
| Publicado en: | Computers in Human Behavior Vol. 52; pp. 184 - 190 |
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| Autores principales: | , , |
| Formato: | Artículo |
| Publicado: |
Elsevier B.V.
Nov2015
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=108809407&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 108809407 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 07475632 JC4 jtl: Computers in Human Behavior issn: 07475632 maglogo: N pubinfo: dt: Nov2015 vid: 52 pid: 2410 pub: Elsevier B.V. artinfo: ui: 108809407 10.1016/j.chb.2015.04.072 ppf: 184 ppct: 6 formats: tig: atl: Measuring influence in online social network based on the user-content bipartite graph. aug: au: Zhu, Zhiguo Su, Jingqin Kong, Liping affil: Faculty of Management and Economics, Dalian University of Technology, 116023 Dalian, PR China School of Management Science and Engineering, Dongbei University of Finance and Economics, 116025 Dalian, PR China Vocational and Technical College, Dongbei University of Finance and Economics, 116025 Dalian, PR China su: Interpersonal relations Social networks Social media sug: subj: Interpersonal relations Social networks Social media Other Individual and Family Services keyword: Bipartite directed graph Influence measurement Markov model Online social network Bipartite directed graph Influence measurement Markov model Online social network ab: With the rising of online social networks, influence has been a complex and subtle force to govern users’ behaviors and relationship formation. Therefore, how to precisely identify and measure influence has been a hot research direction. Differentiating from existing researches, we are devoted to combining the status of users in the network and the contents generated from these users to synthetically measure the influence diffusion. In this paper, we firstly proposed a directed user-content bipartite graph model. Next, an iterative algorithm is designed to compute two scores: the users’ Influence and boards’ Reach. Finally, we conduct extensive experiments on the dataset extracted from the online community Pinterest. The experimental results verify our proposed model can discover most influential users and popular broads effectively and can also be expected to benefit various applications, e.g., viral marketing, personal recommendation, information retrieval, etc. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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