School of Computer Science, University of Technology Sydney, Sydney, NSW, Australia.
Department of Ancient and Modern Civilizations, University of Messina, Messina, Italy.
PLoS One. 2024 Aug 21;19(8):e0308722. doi: 10.1371/journal.pone.0308722. eCollection 2024.
Network disruption is pivotal in understanding the robustness and vulnerability of complex networks, which is instrumental in devising strategies for infrastructure protection, epidemic control, cybersecurity, and combating crime. In this paper, with a particular focus on disrupting criminal networks, we proposed to impose a within-the-largest-connected-component constraint in a continuous batch removal disruption process. Through a series of experiments on a recently released Sicilian Mafia network, we revealed that the constraint would enhance degree-based methods while weakening betweenness-based approaches. Moreover, based on the findings from the experiments using various disruption strategies, we propose a structurally-filtered greedy disruption strategy that integrates the effectiveness of greedy-like methods with the efficiency of structural-metric-based approaches. The proposed strategy significantly outperforms the longstanding state-of-the-art method of betweenness centrality while maintaining the same time complexity.
网络中断在理解复杂网络的鲁棒性和脆弱性方面至关重要，这对于制定基础设施保护、疫情控制、网络安全和打击犯罪的策略具有重要意义。在本文中，我们特别关注中断犯罪网络，提出在连续批量删除中断过程中施加最大连通分量内的约束。通过对最近发布的西西里黑手党网络进行的一系列实验，我们揭示了这种约束将增强基于度数的方法，同时削弱基于介数的方法。此外，基于使用各种中断策略的实验结果，我们提出了一种结构过滤的贪婪中断策略，该策略将贪婪类方法的有效性与基于结构度量的方法的效率相结合。所提出的策略在保持相同时间复杂度的同时，显著优于基于介数中心性的长期以来的最先进方法。
