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TOPIC:
A STRUCTURAL MODEL OF HOMOPHILY AND CLUSTERING IN SOCIAL NETWORKS
ABSTRACT
I develop and estimate a structural model of network formation with heterogeneous players and latent community structure, whose equilibria exhibit levels of sparsity, homophily and clustering that match those usually observed in real-world social networks. Players belong to communities unobserved by the econometrician and have community-specific payoffs, allowing preferences to have a bias for similar people. Players meet sequentially and decide whether to form bilateral links, after receiving a random matching shock. The model converges to a sparse hierarchical exponential family random graph, with weakly dependent links. Using school friendship network data from Add Health, I estimate the posterior distribution of parameters and unobserved heterogeneity, detecting high levels of racial homophily and payoff heterogeneity across communities. The posterior predictions show that the model is able to replicate the homophily levels and the aggregate clustering of the observed network, in contrast with standard exponential family network models.
Keywords: Social networks, Exponential random graphs, Weak dependence, Homophily, Clustering, Sparse networks.