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<p>Identifying influential nodes in complex networks is a critical task with significant applications across various domains. Traditional methods often fail to address the varied nature of node influence, particularly in large-scale networks. To overcome these limitations, this paper presents a machine learning-based method for identifying influential nodes using robust feature engineering and prediction modeling. Specifically, feature vectors are constructed using Extended Degree Centrality (EDC), Eigenvector Centrality (EC), and a composite metric that integrates EDC and VoteRank using a weighting parameter <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\alpha \)</EquationSource> </InlineEquation> together with EC to capture both local and global structural properties of the network. In order to achieve representativeness and scalability, a systematic sampling is proposed and utilized to ensure that nodes with diverse structural and influential properties are included in the training set. Then Random Forest Regressor model employed to predict node influentiality across diverse network datasets. Experimental results on nine real-world network datasets demonstrate the method’s superiority over baseline methods (DC, KS, KSIF, CC, EC, VR, BC, HI, MDD, CR, PR, and RCNN), achieving the highest average Kendall Tau of 0.8158, outperforming the second-best methods by 13.9% over EC and 17.49% over RCNN, and improvements in Jaccard similarity by 13.95% over EC and 8.47% over RCNN. The method’s robustness across network sizes and structures further confirms its scalability and effectiveness.</p>

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Machine Learning-Based Identification of Influential Nodes in Complex Networks

  • 期刊论文

  • 发表日期：

    2026年6月15日

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  • Mohammed A. Ramadhan,
  • Abdulhakeem O. Mohammed

摘要

Identifying influential nodes in complex networks is a critical task with significant applications across various domains. Traditional methods often fail to address the varied nature of node influence, particularly in large-scale networks. To overcome these limitations, this paper presents a machine learning-based method for identifying influential nodes using robust feature engineering and prediction modeling. Specifically, feature vectors are constructed using Extended Degree Centrality (EDC), Eigenvector Centrality (EC), and a composite metric that integrates EDC and VoteRank using a weighting parameter \(\alpha \) together with EC to capture both local and global structural properties of the network. In order to achieve representativeness and scalability, a systematic sampling is proposed and utilized to ensure that nodes with diverse structural and influential properties are included in the training set. Then Random Forest Regressor model employed to predict node influentiality across diverse network datasets. Experimental results on nine real-world network datasets demonstrate the method’s superiority over baseline methods (DC, KS, KSIF, CC, EC, VR, BC, HI, MDD, CR, PR, and RCNN), achieving the highest average Kendall Tau of 0.8158, outperforming the second-best methods by 13.9% over EC and 17.49% over RCNN, and improvements in Jaccard similarity by 13.95% over EC and 8.47% over RCNN. The method’s robustness across network sizes and structures further confirms its scalability and effectiveness.