My friend far, far away: a random field approach to exponential random graph models
研究了大规模人口中网络形成战略模型的渐近性质，提出一种基于logit的简单估计量，在弱同质性条件下具有一致性、一致性和渐近正态性，计算时间短且易于实现。
We explore the asymptotic properties of strategic models of network formation in very large populations. Specifically, we focus on (undirected) exponential random graph models. We want to recover a set of parameters from the individuals' utility functions using the observation of a single, but large, social network. We show that, under some conditions, a simple logit-based estimator is coherent, consistent and asymptotically normally distributed under a weak version of homophily. The approach is compelling as the computing time is minimal and the estimator can be easily implemented using pre-programmed estimators available in most statistical packages. We provide an application of our method using the Add Health database.