A Structural Model of Homophily and Clustering in Social Networks
构建并估计了一个社交网络形成模型，包含异质性参与者和潜在社区结构，能匹配现实网络中的同质性和聚类水平。利用Add Health学校友谊数据，估计了参数和未观测异质性，发现模型能复制观测网络的同质性水平和整体聚类，优于无社区结构的标准指数族网络模型。
I develop and estimate a structural model of network formation with heterogeneous players and latent community structure, whose equilibrium homophily and clustering levels 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 hierarchical exponential family random graph. 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 of sufficient statistics 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 without community structure.