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Resilience centrality in complex networks.

Yongtao Zhang1, Cunqi Shao1, Shibo He1

  • 1State Key Laboratory of Industrial Control Technology, Department of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China.

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Summary
This summary is machine-generated.

We developed resilience centrality, a new index to measure how nodes affect system resilience. This index accurately quantifies node contributions, considering both network structure and system dynamics for better collapse prevention.

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Area of Science:

  • Complex systems science
  • Network science
  • Systems engineering

Background:

  • System resilience is crucial for maintaining functionality during component failures.
  • Existing centrality measures inadequately capture node influence on resilience due to ignoring dynamic characteristics.
  • Diverse node contributions to system resilience pose a challenge for effective protection strategies.

Purpose of the Study:

  • To propose a novel, physically insightful centrality index to quantify node contributions to system resilience.
  • To develop a method that integrates network structure with dynamic characteristics for accurate resilience assessment.
  • To provide a tool for identifying critical nodes for targeted protection in complex systems.

Main Methods:

  • Derivation of a new centrality index, resilience centrality, from a 1D dynamic equation.
  • Quantification of node resilience contribution by integrating network structure (degree, weighted nearest-neighbor degree) and dynamic properties.
  • Validation using four real-world networks representing diverse complex systems.

Main Results:

  • Resilience centrality accurately quantifies the impact of nodes on system resilience.
  • Weighted nearest-neighbor degree significantly influences resilience centrality.
  • The effect of node degree on resilience centrality is modulated by the average weighted degree parameter (βeff).

Conclusions:

  • Resilience centrality offers a more accurate assessment of node importance for system resilience compared to traditional methods.
  • The findings provide theoretical insights for protecting complex systems from collapse by identifying critical nodes.
  • This index enhances our understanding of the interplay between network topology and system dynamics in determining resilience.