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.

该研究旨在改善异构特征表征原则下基于数字孪生的复杂网络系统中节点特征的表达能力，并通过对真实节点特征分布的各种表征原则和优化特征偏好建立数字孪生系统，以重新创建不同国家的真实物理接触网络，并在最流行的节点开始的疫情爆发中调查其各自的抗灾能力。结果表明，使用模糊集增强特征表达的灵活性可提高表达能力并实现更准确的建模，异构特征影响网络结构和疫情爆发速度，需要针对不同的人群采取各种缓解政策。

数字孪生导向的复杂网络系统的异构特征表示

Heterogeneous Feature Representation for Digital Twin-Oriented Complex  Networked Systems

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.

使用可扩展的建模框架提出了数字孪生导向的复杂网络系统，以生成真实系统的网络。通过节点的特征和交互规则来创建连接，实验结果表明结构和动力学的复杂性水平影响网络增长和流行病传播，应针对具有优先特征的节点制定灾难恢复策略和流行病控制。