Presented at the 4th International Conference on Cross-Cultural Decision Making (CCDM), Las Vegas, NV (July 2015)

Since the launch of Facebook in 2004 and Twitter in 2006, the amount of publicly available social network data has grown in both scale and complexity. This growth presents significant challenges to conventional network analysis methods that rely primarily on structure. In this paper, we describe a generative model that extends structure-based connection preference methods to include preferences based on agent similarity or homophily. We also discuss novel methods for extracting model parameters from existing large scale networks (e.g. Twitter) to improve model accuracy. We demonstrate the validity of our proposed extensions and parameter extraction methods by comparing model-generated networks with and without the extensions to real-life networks based on metrics for both structure and homophily. Finally we discuss the potential implications for including homophily in models of social networks and information propagation.