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.

翻译：基于智能体的模型（ABMs）通过将智能体行为与全局观测解耦，在基本层面上模拟社会过程的形成与演化。当ABM中的网络随时间随智能体状态变化（或与之共同演化）时，理解动态过程与网络结构之间的关系至关重要。社会网络为理解这些系统的涌现关系提供了天然的工具集。本研究探讨了一系列网络比较方法在跟踪ABM中网络随时间变化或不同模型参数间变化的效用。被考察的技术中包含一种基于热核渐近量的新型图伪度量，该度量已被证明能区分许多非同构的等谱图。此外，我们确立了利用网络科学中关于真实网络观测结果（例如厚尾度分布、小世界特性）在缺乏经验人口数据时进行ABM验证的方法。所有这些方法均通过在俄亥俄州辛辛那提市2万名智能体群体中模拟友谊形成的原始模型上进行系统性扰动展示。