Social Network Analysis and Validation of an Agent-Based Model

TL;DR

This paper explores methods for analyzing and validating agent-based social network models by comparing network structures over time and with real-world data, introducing a novel graph metric based on heat content asymptotics.

Contribution

It introduces a new graph pseudometric based on heat content asymptotics and demonstrates how social network analysis tools can validate ABMs without empirical data.

Findings

The heat content asymptotics metric effectively distinguishes non-isomorphic graphs.

Network comparison methods can track structural changes in ABMs over time.

Empirical network properties can validate models when real data is unavailable.

Abstract

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.