Heterogeneous Feature Representation for Digital Twin-Oriented Complex Networked Systems

TL;DR

This paper enhances the modeling of complex networked systems by integrating heterogeneous feature representations, including fuzzy sets, to improve realism and resilience analysis of epidemic spread.

Contribution

It introduces a novel heterogeneous feature representation approach combining crisp and fuzzy features for digital twin models of complex systems.

Findings

Fuzzy set features improve model expressiveness and accuracy.

Heterogeneous features affect network structure and epidemic dynamics.

Flexible feature representation aids in designing targeted mitigation policies.

Abstract

Building models of Complex Networked Systems (CNS) that can accurately represent reality forms an important research area. To be able to reflect real world systems, the modelling needs to consider not only the intensity of interactions between the entities but also features of all the elements of the system. This study aims to improve the expressive power of node features in Digital Twin-Oriented Complex Networked Systems (DT-CNSs) with heterogeneous feature representation principles. This involves representing features with crisp feature values and fuzzy sets, each describing the objective and the subjective inductions of the nodes' features and feature differences. Our empirical analysis builds DT-CNSs to recreate realistic physical contact networks in different countries from real node feature distributions based on various representation principles and an optimised feature preference. We also investigate their respective disaster resilience to an epidemic outbreak starting from the most popular node. The results suggest that the increasing flexibility of feature representation with fuzzy sets improves the expressive power and enables more accurate modelling. In addition, the heterogeneous features influence the network structure and the speed of the epidemic outbreak, requiring various mitigation policies targeted at different people.