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