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<p>Identifying influential nodes in complex networks, such as social networks, is crucial for applications like information dissemination, virus protection, and community detection. Numerous methods have been proposed for this task; however, most existing approaches rely on a single nodal attribute, which may lack comprehensiveness and often results in low resolution in distinguishing node influence. To address this limitation, this paper proposes a novel method named the <i>CI</i>-based gravity model (<i>CIGM</i>), which combines the integrated degree and the <i>I</i>-shell index to holistically assess the influence of nodes. The CIGM is designed to provide a more accurate and discriminative measure of node influence compared to existing techniques. We evaluated the performance of CIGM against ten baseline methods on twelve real-world datasets, using evaluation metrics including the SIR model, Kendall’s correlation coefficient, complementary cumulative distribution function (CCDF), and monotonicity index. Experimental results demonstrate that CIGM not only effectively identifies influential nodes but also excels in pinpointing strategically important nodes that enhance information propagation and network connectivity. These findings indicate that CIGM offers superior performance and broader applicability over existing schemes.</p>

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A novel ranking scheme for identifying influential nodes in complex networks

  • 期刊论文

  • 发表日期：

    2025年12月9日

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  • Qing Yang,
  • Jiafei Liu,
  • Dajin Wang,
  • Jingli Wu,
  • Gaoshi Li

摘要

Identifying influential nodes in complex networks, such as social networks, is crucial for applications like information dissemination, virus protection, and community detection. Numerous methods have been proposed for this task; however, most existing approaches rely on a single nodal attribute, which may lack comprehensiveness and often results in low resolution in distinguishing node influence. To address this limitation, this paper proposes a novel method named the CI-based gravity model (CIGM), which combines the integrated degree and the I-shell index to holistically assess the influence of nodes. The CIGM is designed to provide a more accurate and discriminative measure of node influence compared to existing techniques. We evaluated the performance of CIGM against ten baseline methods on twelve real-world datasets, using evaluation metrics including the SIR model, Kendall’s correlation coefficient, complementary cumulative distribution function (CCDF), and monotonicity index. Experimental results demonstrate that CIGM not only effectively identifies influential nodes but also excels in pinpointing strategically important nodes that enhance information propagation and network connectivity. These findings indicate that CIGM offers superior performance and broader applicability over existing schemes.