Inverse model for network construction: ({\delta}(G), I' (G)) -> G

TL;DR

This paper introduces an inverse model using evolutionary computing to construct networks based on isolated toughness and minimum degree, addressing a gap in network design methods.

Contribution

It presents a novel inverse modeling approach for network construction from key parameters, utilizing evolutionary algorithms with pseudo-greedy, mutation, and diversity modules.

Findings

Algorithm effectively constructs networks with desired properties.

Pilot experiments verify the practicality of the proposed method.

Code is publicly available for reproducibility.

Abstract

The isolated toughness variant is a salient parameter for measuring the vulnerability of networks, which is inherently related to fractional factors (used to characterize the feasibility of data transmission). The combination of minimum degree and the corresponding tight bound of isolated toughness variant for fractional factors provide reference standards for network construction. However, previous advances only focused on how to select the optimal parameter criteria from Pareto front, without any suggestion for the construction of specific networks. To overcome this deficiency, this paper proposes an inverse model from $(\delta(G),I'(G))$ to $G$, by means of evolutionary computing approach, we propose a novel inverse model to obtain the optimal solutions for candidate graphs. The main procedure is composed of pseudo-greedy acceleration, cross-mutation and diversity enhancement modules. The practicality of the algorithm is verified by means of pilot experiments. The code in this paper is made public on https://github.com/AizhEngHN/Inverse-model-for-network-construction-G-I-G-G.