  Agent-based models (ABMs) simulate the formation and evolution of social
processes at a fundamental level by decoupling agent behavior from global
observations. In the case where ABM networks evolve over time as a result of
(or in conjunction with) agent states, there is a need for understanding the
relationship between the dynamic processes and network structure. Social
networks provide a natural set of tools for understanding the emergent
relationships of these systems. This work examines the utility of a collection
of network comparison methods for the purpose of tracking network changes in an
ABM over time or between model parameters. Among the techniques examined is a
novel graph pseudometric based on heat content asymptotics, which have been
shown to distinguish many isospectral graphs which are not isomorphic.
Additionally, we establish the use of observations about real-world networks
from network science (e.g. fat-tailed degree distribution, small-world
property) for ABM validation in the case where empirical population data is
unavailable. These methods are all demonstrated on systematic perturbations of
an original model simulating the formation of friendships in a population of
20,000 agents in Cincinnati, OH.
