  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 study. We
subsequently extend one of the most promising simulators from the evaluated
ones, to facilitate generation of social networks of varied structural
complexity levels. This extension brings us one step closer to a Digital Twin
Oriented SNS (DT Oriented SNS). We also propose an approach to assess the
similarity between real and simulated networks with the composite performance
indexes based on both global and local structural measures, while taking
runtime of the simulator as an indicator of its efficiency. We illustrate
various characteristics of the proposed DT Oriented SNS using a well known
Karate Club network as an example. While not considered to be of sufficient
complexity, the simulator is intended as one of the first steps on a journey
towards building a Digital Twin of a social network that perfectly mimics the
reality.
