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<p>Cloud-based networks need information diffusion analysis to study how decisions and influential ideas spread throughout their structures. The presented research merges NetDegree centrality measurement with an IDT-based method as a new methodology for influential node detection. Node features on degree and neighborhood connectivity appear through the NetDegree centrality and the IDT model uses node-level propagation capability for simulation of diffusion processes. Performance evaluation of the proposed method uses various real-world datasets alongside standard centrality metric comparisons for assessment purposes. The approach demonstrates competitive accuracy for identifying key influencers among network entities particularly when dealing with heterogeneous network features. This method operates efficiently using computation power while needing no parameter adjustments for working with various types of networks. The discovered results indicate its potential usage as an efficient practical method for influence maximization applications and related network science applications.</p>

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Prioritizing organizational users with information dissemination tree-based network analysis

  • 期刊论文

  • 发表日期：

    2025年8月11日

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  • Aaquib Hussain Ganai,
  • Monica Bhutani,
  • Hilal Ahmad Khanday,
  • Hufsa Manzoor

摘要

Cloud-based networks need information diffusion analysis to study how decisions and influential ideas spread throughout their structures. The presented research merges NetDegree centrality measurement with an IDT-based method as a new methodology for influential node detection. Node features on degree and neighborhood connectivity appear through the NetDegree centrality and the IDT model uses node-level propagation capability for simulation of diffusion processes. Performance evaluation of the proposed method uses various real-world datasets alongside standard centrality metric comparisons for assessment purposes. The approach demonstrates competitive accuracy for identifying key influencers among network entities particularly when dealing with heterogeneous network features. This method operates efficiently using computation power while needing no parameter adjustments for working with various types of networks. The discovered results indicate its potential usage as an efficient practical method for influence maximization applications and related network science applications.