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Social networks as a representation of relational data, often possessmultiple types of dependency structures at the same time. There could beclustering (beyond homophily) at a macro level as well as transitivity (afriend's friend is more likely to be also a friend) at a micro level. Motivatedby \cite{schweinberger2015local} which constructed a family of ExponentialRandom Graph Models (ERGM) with local dependence assumption, we argue that thiskind of hierarchical models has potential to better fit real networks. Totackle the non-scalable estimation problem, the cost paid for modeling power,we propose a two-stage working model strategy that first utilize Latent SpaceModels (LSM) for their strength on clustering, and then further tune ERGM toarchive goodness of fit.

A two-stage working model strategy for network analysis under Hierarchical Exponential Random Graph Models