Hayashi Yukio, Tanaka Atsushi, Matsukubo Jun
Graduate School of Science and Technology, Japan Advanced Institute of Science and Technology, Nomi 923-1292, Japan.
Graduate School of Science and Engineering, Yamagata University, Yonezawa 992-8510, Japan.
Entropy (Basel). 2021 Jan 12;23(1):102. doi: 10.3390/e23010102.
Complex network infrastructure systems for power supply, communication, and transportation support our economic and social activities; however, they are extremely vulnerable to frequently increasing large disasters or attacks. Thus, the reconstruction of a damaged network is more advisable than an empirically performed recovery of the original vulnerable one. To reconstruct a sustainable network, we focus on enhancing loops so that they are not trees, which is made possible by node removal. Although this optimization corresponds with an intractable combinatorial problem, we propose self-healing methods based on enhancing loops when applying an approximate calculation inspired by statistical physics. We show that both higher robustness and efficiency are obtained in our proposed methods by saving the resources of links and ports when compared to ones in conventional healing methods. Moreover, the reconstructed network can become more tolerant than the original when some damaged links are reusable or compensated for as an investment of resource. These results present the potential of network reconstruction using self-healing with adaptive capacity in terms of resilience.
用于供电、通信和交通的复杂网络基础设施系统支撑着我们的经济和社会活动；然而，它们极易受到频繁增加的重大灾害或攻击的影响。因此，相比于凭经验对原本易受攻击的网络进行恢复，重建受损网络更为可取。为了重建一个可持续的网络，我们专注于增强回路，使其不再是树状结构，这可以通过移除节点来实现。尽管这种优化对应着一个难以处理的组合问题，但在应用受统计物理学启发的近似计算时，我们提出了基于增强回路的自愈方法。我们表明，与传统修复方法相比，我们提出的方法通过节省链路和端口资源，实现了更高的鲁棒性和效率。此外，当一些受损链路可重复使用或作为资源投入得到补偿时，重建后的网络可能比原来的网络更具耐受性。这些结果展现了在恢复力方面利用具有自适应能力的自愈进行网络重建的潜力。
