Review and Assessment of Digital Twin--Oriented Social Network Simulators
Jiaqi Wen, Bogdan Gabrys, Katarzyna Musial

TL;DR
This paper reviews and assesses social network simulators within a Digital Twin framework, extending a promising simulator to generate networks of varying complexity and proposing a similarity assessment method.
Contribution
It provides a critical review of existing SNSs, extends a leading simulator for varied complexity, and introduces a new similarity assessment approach within a Digital Twin context.
Findings
Extended a social network simulator for varied structural complexity.
Proposed a composite index for real vs. simulated network similarity.
Illustrated the approach using the Karate Club network.
Abstract
The ability to faithfully represent real social networks is critical from the perspective of testing various what-if scenarios which are not feasible to be implemented in a real system as the system's state would be irreversibly changed. High fidelity simulators allow one to investigate the consequences of different actions before introducing them to the real system. For example, in the context of social systems, an accurate social network simulator can be a powerful tool used to guide policy makers, help companies plan their advertising campaigns or authorities to analyse fake news spread. In this study we explore different Social Network Simulators (SNSs) and assess to what extent they are able to mimic the real social networks. We conduct a critical review and assessment of existing Social Network Simulators under the Digital Twin-Oriented Modelling framework proposed in our previous…
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Taxonomy
TopicsSoftware-Defined Networks and 5G
