Analyzing the Robustness of Complex Networks with Attack Success Rate
1State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China.
Entropy (Basel, Switzerland)
|November 24, 2023
Summary
This study introduces a new network robustness measure, Robustness-ASR (RASR), accounting for attack success rates. A parallel algorithm, PRQMC, efficiently computes RASR for large networks, improving upon existing methods.
Area of Science:
- Network Science
- Complex Systems Analysis
- Computational Mathematics
Background:
- Network robustness analysis is crucial for understanding network resilience against failures and attacks.
- Existing robustness measures often overlook the variable Attack Success Rate (ASR), assuming attacks are always successful.
- Real-world network attacks exhibit varying success probabilities, necessitating more realistic assessment methods.
Purpose of the Study:
- To propose a novel network robustness measure, Robustness-ASR (RASR), that incorporates the Attack Success Rate (ASR) of individual nodes.
- To develop an efficient parallel algorithm (PRQMC) for computing RASR in large-scale complex networks.
- To introduce a new attack strategy (HBnnsAGP) for evaluating the lower bound of network RASR.
Main Methods:
- Utilizing mathematical expectations to define the Robustness-ASR (RASR) metric.
- Employing randomized quasi-Monte Carlo (RQMC) integration for efficient and accurate RASR approximation.
- Developing a parallel algorithm (PRQMC) to accelerate RASR computation on large networks.
- Introducing the HBnnsAGP attack strategy to establish a lower bound for network RASR.
Main Results:
- The proposed RASR measure effectively quantifies network robustness under variable attack success probabilities.
- The PRQMC algorithm demonstrates significant efficiency gains in computing RASR for large networks.
- Experimental validation on six real-world networks confirms the effectiveness of RASR and PRQMC compared to existing methods.
- The HBnnsAGP strategy provides a tighter lower bound for network robustness assessment.
Conclusions:
- The novel RASR metric offers a more realistic evaluation of network robustness by considering ASR.
- The PRQMC algorithm provides a scalable and efficient solution for analyzing large complex networks.
- The study advances network science by introducing improved methods for robustness assessment and attack strategy evaluation.
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