The Target Recovery Strategy for Preventing Avalanche Breakdown on Interdependent Community Networks. (8th September 2020)

• Record Type: Journal Article
• Authors: Gong, Kai; Huang, Yu; Chen, Xiao-long; Li, Qing; Tang, Ming
• Other Names: Me trovi Ana Academic Editor.
• Is Part Of: Complexity. Volume 2020(2020)
• Publication Date: 2020-09-08
• Subjects: Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1155/2020/1646930
• Languages: English
• ISSNs: 1076-2787

Abstract

Abstract : 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.