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.
@cite_45 showed that there is a significant difference in how hard it is to shatter social networks as compared to infrastructural networks (transportation, power, and wireless radio networks), by examining the size of the largest component in the network as nodes are deleted in order of (residual) degree. Social networks were shown to be much more robust than the infrastructural networks.
@cite_45
2098109664
Over the last fifteen years, researchers in the Basic and Applied Simulation Science group at Los Alamos National Laboratory have worked with various collaborators on the modeling, simulation, and development of associated decision support tools for understanding large socio-technical systems. The extremely detailed multiscale computer simulations allow individual agents (e.g., people, cars, digital devices) to interact among themselves, as well as with the environment and the networked infrastructure. Such simulations are helpful to policy makers and infrastructure planners who need to answer specific questions. Additionally, our formal results show that simulation-based methods are both necessary and sufficient for understanding the dynamics of such complex systems [4–5]. The mathematical and computational theory views simulations as certain kinds of discrete dynamical systems and provides formal methods for the design, specification, and analysis of such simulations [3–5,11]. Unlike physical systems, socio-technical systems are affected not only by physical laws but also by human behavior, regulatory agencies, and government and private enterprise. The simulation of such systems thus presents novel challenges to researchers. Urban transportation systems constitute a canonical example of the types and levels of interactions that characterize these systems: Traffic rules in distant parts of a city can have an important bearing on traffic congestion downtown, and seemingly “reasonable”