Using Aggregated Relational Data to Feasibly Identify Network Structure without Network Data
提出一种利用聚合关系数据（询问受访者其联系人有某特征的数量）的低成本方法，恢复网络形成模型参数，从而推断网络结构，并通过两个现场实验验证其有效性。
Social network data are often prohibitively expensive to collect, limiting empirical network research. We propose an inexpensive and feasible strategy for network elicitation using Aggregated Relational Data (ARD): responses to questions of the form “how many of your links have trait k ?” Our method uses ARD to recover parameters of a network formation model, which permits sampling from a distribution over node- or graph-level statistics. We replicate the results of two field experiments that used network data and draw similar conclusions with ARD alone.