More Tolerant Reconstructed Networks by Self-Healing against Attacks in Saving Resource

TL;DR

This paper introduces self-healing methods for network reconstruction that enhance loops to improve robustness and resource efficiency, making networks more tolerant to attacks and disasters.

Contribution

It proposes a novel approximate calculation approach inspired by statistical physics to enhance network loops, improving resilience and resource efficiency.

Findings

Higher robustness and efficiency in reconstructed networks.

Reconstructed networks can surpass original networks in tolerance.

Resource-saving methods outperform conventional healing techniques.

Abstract

Complex network infrastructure systems for power-supply, communication, and transportation support our economical and social activities, however they are extremely vulnerable against the frequently increasing large disasters or attacks. Thus, a reconstructing from damaged network is rather advisable than empirically performed recovering to the original vulnerable one. In order to reconstruct a sustainable network, we focus on enhancing loops so as not to be trees as possible by node removals. Although this optimization is corresponded to an intractable combinatorial problem, we propose self-healing methods based on enhancing loops in applying an approximate calculation inspired from a statistical physics approach. We show that both higher robustness and efficiency are obtained in our proposed methods with saving the resource of links and ports than ones in the conventional healing methods. Moreover, the reconstructed network by healing can become more tolerant than the original one before attacks, when some extent of damaged links are reusable or compensated as investment of resource. These results will be open up the potential of network reconstruction by self-healing with adaptive capacity in the meaning of resilience.