Recovering Unobserved Network Links from Aggregated Relational Data: Discussions on Bayesian Latent Surface Modeling and Penalized Regression

Abstract

Accurate network data are essential in fields such as economics, sociology, and computer science. Aggregated Relational Data (ARD) provides a way to capture network structures using partial data. This article compares two main frameworks for recovering network links from ARD: Bayesian Latent Surface Modeling (BLSM) and Frequentist Penalized Regression (FPR). Using simulation studies and real-world applications, we evaluate their theoretical properties, computational efficiency, and practical utility in domains like financial risk assessment and epidemiology. Key findings emphasize the importance of trait design, privacy considerations, and hybrid modeling approaches to improve scalability and robustness.