An Empirical Assessment of the Complexity and Realism of Synthetic Social Contact Networks
This work addresses the challenge of creating realistic synthetic social networks for researchers in fields like epidemiology and urban planning, though it is incremental as it builds on existing network generation methods.
The authors tackled the problem of evaluating how realistic synthetically-generated social contact networks are compared to real-world networks, finding that their synthetic networks, created by integrating census, transportation, and geographical data, are closer to real-world graphs across multiple structural measures than common stylized network models.
We use multiple measures of graph complexity to evaluate the realism of synthetically-generated networks of human activity, in comparison with several stylized network models as well as a collection of empirical networks from the literature. The synthetic networks are generated by integrating data about human populations from several sources, including the Census, transportation surveys, and geographical data. The resulting networks represent an approximation of daily or weekly human interaction. Our results indicate that the synthetically generated graphs according to our methodology are closer to the real world graphs, as measured across multiple structural measures, than a range of stylized graphs generated using common network models from the literature.