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A Multi-Attribute Decision-Making Approach for Critical Node Identification in Complex Networks.

Xinyun Zhao1, Yongheng Zhang1, Qingying Zhai2

  • 1Electronic Engineering Institute, National University of Defense Technology, Hefei 230037, China.

Entropy (Basel, Switzerland)
|January 8, 2025
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Summary

Identifying influential nodes is key for network security. A new multi-attribute indicator, MCTNDI, offers a more comprehensive approach than single-attribute methods, improving critical node identification in complex networks.

Keywords:
complex networkscritical node identificationmulti-attribute decision makingnode importance indicator

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Area of Science:

  • Network Science
  • Complex Systems Analysis
  • Information Security

Background:

  • Identifying influential nodes is crucial for network security and targeted protection.
  • Existing centrality measures (degree, closeness, betweenness, H-index, K-shell) offer limited perspectives on node importance.
  • A comprehensive evaluation of node importance in complex networks is currently lacking.

Purpose of the Study:

  • To propose a novel multi-attribute indicator, the Multi-Attribute CRITIC-TOPSIS Network Decision Indicator (MCTNDI), for identifying critical nodes.
  • To overcome the limitations of single-attribute indicators by integrating multiple network perspectives.
  • To provide a more accurate and comprehensive measure of node importance in complex networks.

Main Methods:

  • Integration of closeness centrality, betweenness centrality, H-index, and network constraint coefficients.
  • Development of the Multi-Attribute CRITIC-TOPSIS Network Decision Indicator (MCTNDI).
  • Validation using real-world network datasets (Contiguous USA, Dolphins, USAir97, Tech-routers-rf).

Main Results:

  • MCTNDI effectively identifies critical nodes by combining local neighborhood importance, topological location, path centrality, and node mutual information.
  • Validation demonstrated MCTNDI's superior performance in simulated network attacks and node importance distribution compared to existing methods.
  • Analysis of ranking monotonicity and indicator similarity confirmed the robustness and comprehensiveness of MCTNDI.

Conclusions:

  • The proposed MCTNDI provides a more holistic and accurate assessment of node importance in complex networks.
  • MCTNDI addresses the one-sidedness of traditional indicators, enhancing network security analysis.
  • The indicator's effectiveness is validated across diverse real-world networks, suggesting broad applicability.