Fuzzy Feature Representation for Digital Twin-Oriented Social Network Simulators

Building Social Network Simulators (SNSs) 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 Social Network Simulators (DT-SNSs) 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-SNSs to recreate realistic physical contact networks in different countries from real node feature distributions based on various representation principles and an optimised feature preference. 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, where nodes with preferred features and similar features cluster.