基于惩罚回归的聚集关系数据网络结构恢复
Recovering Network Structure from Aggregated Relational Data using Penalized Regression
摘要：
收集社交网络数据的成本可能很高。Breza et al.（2017）提出了聚合关系数据（ARD）作为一种低成本的替代品，可用于恢复由特定参数随机效应模型生成的潜在社交网络的结构。我们的主要观察是，许多经济网络形成模型产生的网络是有效的低秩。因此，从ARD的网络恢复通常是可能的，而不需要使用核范数惩罚回归的参数假设。我们演示了如何实现这种方法，并提供有限样本的网络链接的分布估计的均方误差的界限。对于具有数百个观测值的样本，计算只需几秒钟。易于使用的R和Python代码可以在https://github.com/mpleung/ARD上找到。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
