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Non-Markovian random walks characterize network robustness to nonlocal cascades
Angelo Valente1, Manlio De Domenico2, Oriol Artime3
1Department of Mathematics, University of Trento, 38123 Povo (TN), Italy.
Physical Review. E
|May 20, 2022
Summary
This study introduces a new dynamical model for understanding cascade failures in complex networks. The model captures nonlocal jumps, improving predictions of system vulnerability and failure dynamics.
Area of Science:
- Complex systems science
- Network science
- Statistical physics
Background:
- Real-world networks face cascade failures, impacting system operations.
- Existing models often use static or local failure propagation, missing nonlocal dynamics.
Purpose of the Study:
- To develop a dynamical model for nonlocal cascade failures.
- To characterize critical behavior and stopping times of cascades.
- To quantify system vulnerability in complex networks.
Main Methods:
- A dynamical model simulating failure propagation as a self-avoiding random walk.
- Incorporation of nonlocal jumps to operational network units.
- Numerical experiments on synthetic and empirical networks (biological, transportation).
Main Results:
- The model successfully captures nonlocal cascade failure dynamics.
- Characterization of critical behavior out of equilibrium.
- Accurate prediction of cascade stopping time distributions.
Conclusions:
- The proposed framework effectively quantifies vulnerability to nonlocal cascade failures.
- The model offers a more realistic approach to network failure analysis.
- Demonstrated applicability across diverse complex network types.
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