论文标题
使用惩罚回归从汇总的关系数据中恢复网络结构
Recovering Network Structure from Aggregated Relational Data using Penalized Regression
论文作者
论文摘要
社交网络数据可能很昂贵。 Breza等。 （2017年）将汇总关系数据（ARD）作为一种低成本替代品，可用于通过特定参数随机效应模型生成潜在社交网络的结构。我们的主要观察结果是，许多经济网络形成模型产生的网络实际上是低级的。结果，没有使用核电 - 惩罚回归的参数假设，从ARD进行网络恢复通常是可能的。我们演示了如何实现此方法并在网络链接分布的结果估计器的平均误差上提供有限样本界限。计算需要数百个观测值的样本。可以在https://github.com/mpleung/ard上找到R和Python中的易于使用的代码。
Social network data can be expensive to collect. Breza et al. (2017) propose aggregated relational data (ARD) as a low-cost substitute that can be used to recover the structure of a latent social network when it is generated by a specific parametric random effects model. Our main observation is that many economic network formation models produce networks that are effectively low-rank. As a consequence, network recovery from ARD is generally possible without parametric assumptions using a nuclear-norm penalized regression. We demonstrate how to implement this method and provide finite-sample bounds on the mean squared error of the resulting estimator for the distribution of network links. Computation takes seconds for samples with hundreds of observations. Easy-to-use code in R and Python can be found at https://github.com/mpleung/ARD.
