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Effect of the social influence on topological properties of user-object bipartite networks

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  • Jian-Guo Liu
  • Zhaolong Hu
  • Qiang Guo

Abstract

Social influence plays an important role in analyzing online users’ collective behaviors [Salganik et al., Science 311, 854 (2006)]. However, the effect of the social influence from the viewpoint of theoretical model is missing. In this paper, by taking into account the social influence and users’ preferences, we develop a theoretical model to analyze the topological properties of user-object bipartite networks, including the degree distribution, average nearest neighbor degree and the bipartite clustering coefficient, as well as topological properties of the original user-object networks and their unipartite projections. According to the users’ preferences and the global ranking effect, we analyze the theoretical results for two benchmark data sets, Amazon and Bookcrossing, which are approximately consistent with the empirical results. This work suggests that this model is feasible to analyze topological properties of bipartite networks in terms of the social influence and the users’ preferences. Copyright EDP Sciences, SIF, Springer-Verlag Berlin Heidelberg 2013

Suggested Citation

  • Jian-Guo Liu & Zhaolong Hu & Qiang Guo, 2013. "Effect of the social influence on topological properties of user-object bipartite networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 86(11), pages 1-11, November.
    • Handle: RePEc:spr:eurphb:v:86:y:2013:i:11:p:1-11:10.1140/epjb/e2013-40328-4
    DOI: 10.1140/epjb/e2013-40328-4
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    Citations

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    Cited by:

    1. Zhen-Hua Yang & Jian-Guo Liu & Chang-Rui Yu & Jing-Ti Han, 2017. "Quantifying the effect of investors’ attention on stock market," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-16, May.
    2. Du, Ruijin & Dong, Gaogao & Tian, Lixin & Liu, Runran, 2016. "Targeted attack on networks coupled by connectivity and dependency links," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 687-699.
    3. Hu, Liang & Ren, Liang & Lin, Wenbin, 2018. "A reconsideration of negative ratings for network-based recommendation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 690-701.
    4. Chandra, Anita & Garg, Himanshu & Maiti, Abyayananda, 2019. "A general growth model for online emerging user–object bipartite networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 517(C), pages 370-384.
    5. Guo, Qiang & Ji, Lei & Liu, Jian-Guo & Han, Jingti, 2017. "Evolution properties of online user preference diversity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 698-713.
    6. Hou, Lei & Liu, Kecheng & Liu, Jianguo & Zhang, Runtong, 2017. "Solving the stability–accuracy–diversity dilemma of recommender systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 415-424.

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    Keywords

    Statistical and Nonlinear Physics;

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