Digital Twin-Oriented Complex Networked Systems based on Heterogeneous Node Features and Interaction Rules

TL;DR

This paper introduces a flexible framework for modeling digital twin-oriented complex networks with diverse node features and interaction rules, enabling realistic simulations of network growth and epidemic spread.

Contribution

It presents a novel extendable modeling framework for DT-CNSs that incorporates heterogeneous node features and interaction rules, validated through simulations and a case study on epidemic resilience.

Findings

Node feature diversity affects network growth and epidemic dynamics.

Targeted mitigation on high-risk nodes improves disaster resilience.

Interaction rule flexibility influences epidemic spread patterns.

Abstract

This study proposes an extendable modelling framework for Digital Twin-Oriented Complex Networked Systems (DT-CNSs) with a goal of generating networks that faithfully represent real systems. Modelling process focuses on (i) features of nodes and (ii) interaction rules for creating connections that are built based on individual node's preferences. We conduct experiments on simulation-based DT-CNSs that incorporate various features and rules about network growth and different transmissibilities related to an epidemic spread on these networks. We present a case study on disaster resilience of social networks given an epidemic outbreak by investigating the infection occurrence within specific time and social distance. The experimental results show how different levels of the structural and dynamics complexities, concerned with feature diversity and flexibility of interaction rules respectively, influence network growth and epidemic spread. The analysis revealed that, to achieve maximum disaster resilience, mitigation policies should be targeted at nodes with preferred features as they have higher infection risks and should be the focus of the epidemic control.