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Analyzing the Robustness of Complex Networks with Attack Success Rate
Version 1
: Received: 28 September 2023 / Approved: 3 October 2023 / Online: 9 October 2023 (11:37:39 CEST)
A peer-reviewed article of this Preprint also exists.
Yang, F.; Wang, Y. Analyzing the Robustness of Complex Networks with Attack Success Rate. Entropy 2023, 25, 1508. Yang, F.; Wang, Y. Analyzing the Robustness of Complex Networks with Attack Success Rate. Entropy 2023, 25, 1508.
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.
Keywords
complex network; robustness; quasi-Monte Carlo; attack success rate
Subject
Computer Science and Mathematics, Other
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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