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Mining structural influence to analyze relationships in social network

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  • Guo, Lin
  • Zhang, Ben

Abstract

Social influence is a fundamental issue in social network analysis and has attracted tremendous attention. However, existing research mainly focuses on studying peer influence. The method proposed is to analyze the degree of influence between nodes in a low-density network, and then mine structural influence and predict the degree of affect between the center node and others. We evaluate the proposed algorithm on both synthetic and real large networks. Experimental results show that our proposed algorithm has better performance than several alternative algorithms.

Suggested Citation

  • Guo, Lin & Zhang, Ben, 2019. "Mining structural influence to analyze relationships in social network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 301-309.
    • Handle: RePEc:eee:phsmap:v:523:y:2019:i:c:p:301-309
    DOI: 10.1016/j.physa.2019.02.005
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    References listed on IDEAS

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    2. Chris Tofallis, 2015. "A better measure of relative prediction accuracy for model selection and model estimation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(8), pages 1352-1362, August.
    3. A. Belloni & V. Chernozhukov & I. Fernández‐Val & C. Hansen, 2017. "Program Evaluation and Causal Inference With High‐Dimensional Data," Econometrica, Econometric Society, vol. 85, pages 233-298, January.
    4. Chris Tofallis, 2015. "A better measure of relative prediction accuracy for model selection and model estimation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(3), pages 524-524, March.
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    Cited by:

    1. Aziz, Furqan & Gul, Haji & Muhammad, Ishtiaq & Uddin, Irfan, 2020. "Link prediction using node information on local paths," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 557(C).

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