Considering baseline homophily when generating spatial social networks for agent-based modelling

Social networks have become an important part of agent-based models, and their structure may have remarkable impact on simulation results. We propose a simple and efficient but empirically based approach for spatial agent-based models which explicitly takes into account restrictions and opportunities imposed by effects of baseline homophily, i.e. the influence of local socio-demography on the composition of one's social network. Furthermore, the algorithm considers the probability of links that depends on geographical distance between potential partners. The resulting network reflects social settings and furthermore allows the modeller to influence network properties by adjusting agent type specific parameters. Especially the parameter for distance dependence and the probability of distant links allow for control of clustering and agent type distribution of personal networks.