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Examining indicators of complex network vulnerability across diverse attack scenarios
Ahmad F Al Musawi1,2, Satyaki Roy3, Preetam Ghosh4
1Department of Information Technology, University of Thi Qar, Thi Qar, Iraq. almusawiaf@utq.edu.iq.
Scientific Reports
|October 24, 2023
Summary
Complex networks
Area of Science:
- Network science
- Systems analysis
- Graph theory
Background:
- Complex networks model real-world systems across diverse fields.
- Network analysis identifies critical components and vulnerabilities.
- Understanding network structure is key to system integrity.
Purpose of the Study:
- Investigate topological properties determining network vulnerability to attacks.
- Quantify the impact of network growth/depletion on robustness.
- Identify key indicators of network susceptibility.
Main Methods:
- Analysis of topological indicators (e.g., modularity, assortativity, LCC).
- Simulation of node- and link-based attack scenarios.
- Partial least squares discriminant analysis for indicator contribution.
Main Results:
- High modularity and disassortativity strongly correlate with network vulnerability.
- These properties make networks susceptible to targeted attacks.
- Robustness decreases with network depletion under attack.
Conclusions:
- Modularity and disassortativity are key indicators of network vulnerability.
- Findings support strategies to enhance critical infrastructure robustness.
- Adaptive models can defend against strategic attacks.
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