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In this work we study the detection of influencers in weighted networks. These choices are made according to classical topological measures in comparison with evidence based measures that relies on the theory of belief funtions. On a different approach we also consider two algorithms to analyze the influence maximization problem. To model the influence diffusion process we use the linear threshold model on a weighted network, where the weight of a link represents the ability of two nodes to influence each other. Based on this measure we will choose the influencers of the social network by comparing the diffusion results.

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Detecting Influencers in Social Networks Based on Evidential Centrality Measures

  • 会议论文

  • 发表日期：

    2026年

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  • J. Leonel Rocha,
  • S. Carvalho,
  • Beatriz Coimbra

摘要

In this work we study the detection of influencers in weighted networks. These choices are made according to classical topological measures in comparison with evidence based measures that relies on the theory of belief funtions. On a different approach we also consider two algorithms to analyze the influence maximization problem. To model the influence diffusion process we use the linear threshold model on a weighted network, where the weight of a link represents the ability of two nodes to influence each other. Based on this measure we will choose the influencers of the social network by comparing the diffusion results.