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Network reconstruction of social networks based on the public information.
Li, Ren-De; Guo, Qiang; Ma, Hao-Tian; Liu, Jian-Guo.
Afiliação
  • Li RD; Library and Business School, University of Shanghai for Science and Technology, Shanghai 200093, People's Republic of China.
  • Guo Q; Library and Business School, University of Shanghai for Science and Technology, Shanghai 200093, People's Republic of China.
  • Ma HT; Institute of Accounting and Finance, Shanghai University of Finance and Economics, Shanghai 200433, People's Republic of China.
  • Liu JG; Institute of Accounting and Finance, Shanghai University of Finance and Economics, Shanghai 200433, People's Republic of China.
Chaos ; 31(3): 033123, 2021 Mar.
Article em En | MEDLINE | ID: mdl-33810733
The structure of a social network plays a crucial role for dynamic analysis, which is invisible in most scenes. In this paper, we present a model for reconstructing the social network by taking into account the public opinion diffusion dynamic model for specific agenda. First, the initial polarity attitude of users i for the agenda, oi, is set in the range [-1,1], where negative and positive attitudes are set as -1 and 1, respectively, while 0 means that user i's attitude is uncertain. Second, we present an optimization model for detecting the relationship among each pair of users based on the generated public observable information. The experimental results for four synthetic networks and three real-world social networks show that the reconstruction accuracy depends on the uncertainty of the initial attitudes greatly. This work is helpful for revealing the structure of social networks in terms of public information.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Aspecto: Determinantes_sociais_saude Idioma: En Revista: Chaos Assunto da revista: CIENCIA Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Aspecto: Determinantes_sociais_saude Idioma: En Revista: Chaos Assunto da revista: CIENCIA Ano de publicação: 2021 Tipo de documento: Article
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