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Related Concept Videos

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Related Experiment Video

Updated: May 2, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

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Robustness of controllability for networks based on edge-attack.

Sen Nie1, Xuwen Wang1, Haifeng Zhang2

  • 1Department of Modern Physics, University of Science and Technology of China, Hefei, P. R. China.

Plos One
|March 4, 2014
PubMed
Summary

Network controllability under cascading failures is less robust in Erdős-Rényi networks than scale-free networks when facing intentional attacks. Robustness of control is crucial for large-scale cascading failures, impacting driver node increments.

Related Experiment Videos

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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Area of Science:

  • Network science
  • Complex systems analysis
  • Systems engineering

Background:

  • Cascading failures pose significant risks to network stability and functionality.
  • Understanding network controllability is crucial for designing resilient systems.
  • Attacking strategies can compromise network integrity, leading to widespread failures.

Purpose of the Study:

  • To investigate the controllability of networks during cascading failures.
  • To compare the robustness of network controllability under random and intentional attack strategies.
  • To analyze the impact of network structure on controllability during failures.

Main Methods:

  • Simulations were conducted on Erdős-Rényi and Scale-free networks.
  • Two attack strategies were employed: random and highest-load edge attacks.
  • The removal fraction of nodes/edges was systematically increased to observe effects.
  • The number of driver nodes required for control was analyzed.

Main Results:

  • Erdős-Rényi networks with moderate average degree showed less robust controllability under highest-load edge attacks.
  • Scale-free networks with moderate power-law exponents exhibited strong controllability robustness against the same attack.
  • Network vulnerability to random and intentional attacks differed with increasing removal fraction.
  • Robustness of control played a key role in managing large-scale cascades.

Conclusions:

  • Scale-free networks demonstrate superior controllability robustness compared to Erdős-Rényi networks under specific attack scenarios.
  • Network structure, particularly the power-law exponent and strongly connected components, influences controllability during cascading failures.
  • The scale of cascades does not directly correlate with the increment of driver nodes needed for control in scale-free networks.