  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.
