Digital Twin-Oriented Complex Networked Systems
- Publication Type:
- Thesis
- Issue Date:
- 2023
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This research thesis proposes a concept of Digital Twin-Oriented Complex Networked Systems (DT-CNSs) and offers a novel theoretical framework and its implementation for modelling of Complex Networked Systems towards an ultimate goal — a Digital Twin (DT) that can accurately reflect reality. As the first step towards DT-CNSs, a comprehensive literature review on Complex Networked Systems is conducted and a conceptual modelling framework that consists of five generations of DT-CNSs with increasing complexity levels towards a DT is proposed. Under the developed generations of modelling, we explore one of the possible development paths, Digital Twin-Oriented Social Network Simulator (DT-SNS) which is an instantiation of DT-CNS in the context of social networks. This is achieved by a topical review on the current state-of-the- art of Social Network Simulators (SNSs) and an experiment on developing the SNS towards the DT-SNSs. This review reveals a natural development path for DT-CNSs in the context of social networks — to increase the heterogeneity of nodes’ features (characteristics of each specific node) and their preferences to create relationships while allowing the CNSs to evolve with the preference changes under the influence of epidemic transmission. Accordingly, we propose an extensible DT-CNS modelling framework based on heterogeneous node features and interaction rules that characterise nodes’ preferences for connecting with others. We further improve the expressive power of node features in the proposed DT-CNS models by introducing heterogeneous node 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. As the next step, we extend this modelling framework for evolutionary DT-CNSs by introducing changeable interaction rules. This involves the heterogeneous preference mutation mechanisms that model nodes’ adaptive decisions on social contact under the impact of epidemic outbreak. Our empirical analysis of social network simulations and epidemic spreading process on these networks reveals the influence of nodes’ features and interaction rules on the network formation and epidemic spread. The increasing complexity of process and structure while, at the same time, including interrelation between those two enables more robust modelling and better preserves necessary heterogeneous patterns observed in reality. This research project can be further extended towards a DT by incorporating more complex information extracted from the real world.
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