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The Target Recovery Strategy for Preventing Avalanche Breakdown on Interdependent Community Networks
Complexity ( IF 2.3 ) Pub Date : 2020-09-08 , DOI: 10.1155/2020/1646930
Kai Gong 1 , Yu Huang 1 , Xiao-long Chen 1 , Qing Li 2 , Ming Tang 3, 4
Affiliation  

Many real infrastructure systems such as power grids and communication networks across cities not only depend on each other but also have community structures. This observation derives a new research subject of the interdependent community networks (ICNs). Recent works showed that the ICNs are extremely vulnerable to the failure of interconnected nodes between communities. Such vulnerability is prone to cause avalanche breakdown of the ICNs. How to improve the robustness of ICNs remains a challenge. In this paper, we propose a new target recovery strategy in the self-awareness recovery model, called recovery strategy based on community structures (RCS). The self-awareness recovery model repairs and reactivates the original pair of failed nodes that belong to mutual boundary of networks during cascading failures. The key insight is that the RCS explicitly considers both intercommunity links and intracommunity links. In this paper, we compare RCS with the state-of-the-art approaches based on randomness, degree centrality, and local centrality. We find that the RCS outperforms the other three strategies on the size of giant component, the existence probability of giant component, the number of iterative cascade steps, and the average degree of the remaining network. Moreover, RCS is robust against a given noise, and the optimal parameter of RCS remains stable even if the recovery ratio varies.

中文翻译:

防止相互依赖的社区网络上发生雪崩的目标恢复策略

许多真实的基础设施系统,例如城市之间的电网和通信网络,不仅相互依赖,而且具有社区结构。这一观察得出了相互依赖的社区网络(ICN)的新研究主题。最近的工作表明,ICN极易受到社区之间互连节点故障的影响。这种漏洞很容易引起ICN的雪崩击穿。如何提高ICN的鲁棒性仍然是一个挑战。在本文中,我们提出了一种新的自我意识恢复模型中的目标恢复策略,称为基于社区结构的恢复策略(RCS)。自我意识恢复模型修复并重新激活级联故障期间属于网络相互边界的原始故障节点对。关键的见解是,RCS明确考虑了社区间链接和社区内链接。在本文中,我们将RCS与基于随机性,程度中心性和局部中心性的最新方法进行了比较。我们发现,RCS在巨型组件的大小,巨型组件的存在概率,级联迭代的数量以及剩余网络的平均程度方面优于其他三个策略。而且,RCS对给定的噪声具有鲁棒性,即使恢复率变化,RCS的最佳参数也保持稳定。我们发现,RCS在巨型组件的大小,巨型组件的存在概率,级联迭代的数量以及剩余网络的平均程度方面优于其他三个策略。而且,RCS对给定的噪声具有鲁棒性,即使恢复率变化,RCS的最佳参数也保持稳定。我们发现,RCS在巨型组件的大小,巨型组件的存在概率,级联迭代的数量以及剩余网络的平均程度方面优于其他三个策略。而且,RCS对给定的噪声具有鲁棒性,即使恢复率变化,RCS的最佳参数也保持稳定。
更新日期:2020-09-08
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