A Novel Weighted TOPSIS-based Method for Node Centrality Analysis in Complex Networks
DOI:
https://doi.org/10.11113/mjfas.v21n5.4412Keywords:
Complex network, Node centrality, Critical nodes, Benchmark-based weighting, Weighted Modified-TOPSIS (WM-TOPSIS)Abstract
Complex networks are prevalent across domains such as biology, social science, and engineering, where identifying critical nodes is essential for applications such as disease control, traffic optimization, and discovering pivotal proteins. Various centrality metrics exist to assess node importance, each with unique strengths and limitations in capturing significance. Recently, the Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) method is used to pinpoint key nodes by considering centrality measures as multiple attributes of the network. This paper introduces a Weighted Modified-TOPSIS (WM-TOPSIS) method, enhancing TOPSIS by integrating multiple topological centrality measures and proposing a novel benchmark-based, data-driven algorithm to dynamically assign weights to attributes. The effectiveness of WM-TOPSIS was evaluated on Protein-Protein Interaction (PPI) networks of Saccharomyces cerevisiae (Yeast) and Escherichia coli (E. coli). On Yeast, WM-TOPSIS achieved a sensitivity (SN) of 0.5150, specificity (SP) of 0.7455, and accuracy of 0.6927. On E. coli, it recorded an SN of 0.8031, SP of 0.4355, and accuracy of 0.4697. These results highlight WM-TOPSIS’s potential to improve the detection of critical nodes, offering a robust framework for biological network analysis and broader applications.
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Copyright (c) 2025 Abdul Moiz, Ubaida Fatima, Muhammad Zeeshan Ul Haque
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