# Spontaneous recovery in dynamical networks

@article{Majdandzic2014SpontaneousRI, title={Spontaneous recovery in dynamical networks}, author={Antonio Majdandzic and Boris Podobnik and Sergey V. Buldyrev and Dror Y. Kenett and Shlomo Havlin and Harry Eugene Stanley}, journal={Nature Physics}, year={2014}, volume={10}, pages={34-38} }

Networks that fail can sometimes recover spontaneously—think of traffic jams suddenly easing or people waking from a coma. A model for such recoveries reveals spontaneous ‘phase flipping’ between high-activity and low-activity modes, in analogy with first-order phase transitions near a critical point.

#### 216 Citations

Persistent Threshold Dynamics with Recovery in Complex Networks

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- 2019

Threshold rules of spreading in binary-state networks lead to cascades. We study persistent cascade-recovery dynamics on quasi-robust networks, i.e., networks which are robust against small trigger… Expand

Failure-recovery model with competition between failures in complex networks: a dynamical approach

- Mathematics, Physics
- 2016

Real systems are usually composed by units or nodes whose activity can be interrupted and restored intermittently due to complex interactions not only with the environment, but also with the same… Expand

Failure and recovery in dynamical networks

- Computer Science, Medicine
- Scientific reports
- 2017

A unifying theory is developed which links the dynamics of the model to contact processes and may help to better understand controllability in spatially embedded and random networks where spontaneous recovery of components can mitigate spontaneous failure and damage spread in dynamical networks. Expand

Cascading failure and recovery of spatially interdependent networks

- Computer Science
- 2017

Many real networks, such as infrastructure networks, are interdependent and their structure is influenced by spatial constraints. However, the existence of spatial constraints and dependency links… Expand

Vitality of Neural Networks under Reoccurring Catastrophic Failures

- Computer Science, Biology
- Scientific reports
- 2016

It is experimentally shown that excitatory neural networks are governed by a non-Poissonian reoccurrence of catastrophic failures, where their repetition time follows a multimodal distribution characterized by a few tenths of a second and tens of seconds timescales. Expand

Critical transitions in heterogeneous networks: Loss of low-degree nodes as an early warning signal.

- Medicine, Computer Science
- Physical review. E
- 2019

It is suggested that a significant reduction in less connected nodes can indicate impending global failure, and a minimal two-state dynamic on a complex network and the effect of local connectivities on the evolution of network nodes are proposed. Expand

Reviving a failed network via microscopic interventions

- Physics, Mathematics
- 2020

Applied to a range of nonlinear network dynamics, a complex system's recoverable phase is identified, a state in which the system can be reignited by a microscopic intervention, i.e. controlling just a single node. Expand

Predicting Lifetime of Dynamical Networks Experiencing Persistent Random Attacks

- Computer Science, Physics
- ArXiv
- 2014

This work analyzes a class of decaying dynamical networks experiencing persistent attacks in which the magnitude of the attack is quantified by the probability of aninternal failure, and there is some chance that an internal failure will be permanent. Expand

Recovery of Interdependent Networks

- Computer Science, Physics
- Scientific reports
- 2016

A recovery strategy for nodes is introduced and an analytic and numerical framework for studying the concurrent failure and recovery of a system of interdependent networks based on an efficient and practically reasonable strategy is developed. Expand

Recent progress on cascading failures and recovery in interdependent networks

- Computer Science
- International Journal of Disaster Risk Reduction
- 2019

This review will mainly discuss recent progress in understanding the robustness of NON having cascading failures features that are realistic for infrastructure networks. Expand

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