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- W4387650637 abstract "Analyzing network robustness against random failures or malicious attacks is a critical research issue in network science as it helps to enhance the robustness of beneficial networks or efficiently disintegrate harmful networks. Most studies commonly neglect the impact of the attack success rate (ASR) and assume that attacks on the network will always be successful. However, in real-world scenarios, an attack may not always succeed. This paper proposes a novel robustness measure called Robustness-ASR (RASR), which utilizes mathematical expectations to assess network robustness when considering the ASR of each node. To efficiently compute the RASR for large-scale networks, a parallel algorithm named PRQMC is presented, which leverages randomized quasi-Monte Carlo integration to approximate the RASR with a faster convergence rate. Additionally, a new attack strategy named HBnnsAGP is introduced to better assess the lower bound of network RASR. Finally, the experimental results on 6 representative real-world complex networks demonstrate the effectiveness of the proposed methods compared with the state-of-the-art baselines." @default.
- W4387650637 created "2023-10-16" @default.
- W4387650637 creator A5059061641 @default.
- W4387650637 creator A5063788952 @default.
- W4387650637 date "2023-10-09" @default.
- W4387650637 modified "2023-10-16" @default.
- W4387650637 title "Analyzing the Robustness of Complex Networks with Attack Success Rate" @default.
- W4387650637 doi "https://doi.org/10.20944/preprints202310.0451.v1" @default.
- W4387650637 hasPublicationYear "2023" @default.
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