Robustness and Vulnerability of Networks with Dynamical Dependency Groups.
Ya-Nan Bai1, Ning Huang1,2, Lei Wang3
1School of Reliability and Systems Engineering, Beihang University, Beijing, 100191, P.R. China.
Scientific Reports
|November 29, 2016
Summary
This study introduces an evolving network model to analyze network robustness, incorporating time-dependent failures and recovery. Larger dependency groups can increase vulnerability or robustness depending on the transition point.
Area of Science:
- Network Science
- Complex Systems
- Statistical Physics
Background:
- Network robustness is crucial, influenced by node failures and recovery.
- Existing models often overlook time-dependent node interdependencies and realistic recovery processes.
Purpose of the Study:
- To develop an evolving network model that captures time-varying dependencies and recovery mechanisms.
- To analyze the impact of these dynamic factors on overall network robustness.
Main Methods:
- Developed an evolving network model with failure and recovery mechanisms.
- Utilized generating function techniques for an analytical framework applicable to random networks.
- Employed numerical simulations on Erdős-Rényi and Barabási-Albert networks for validation.
Main Results:
- Identified an abrupt percolation transition in networks with dynamical dependency groups.
- Demonstrated that the impact of dependency group size on vulnerability is contingent on the transition point relative to the failure threshold.
Conclusions:
- The evolving network model provides a more realistic assessment of network robustness.
- Network robustness is significantly affected by the interplay of time-dependent dependencies and recovery processes.
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