Network regression and supervised centrality estimation
Abstract
The centrality in a network is often used to measure nodes' importance and model network effects on a certain outcome. Empirical studies widely adopt a two-stage procedure, which first estimates the centrality from the observed noisy network and then infers the network effect from the estimated centrality, even though it lacks theoretical understanding. We propose a unified modeling framework to study the properties of centrality estimation and inference and the subsequent network regression analysis with noisy network observations. Furthermore, we propose a supervised centrality estimation methodology, which aims to simultaneously estimate both centrality and network effect. We showcase the advantages of our method compared with the two-stage method both theoretically and numerically via extensive simulations and a case study in predicting currency risk premiums from the global trade network.
Cite
@article{arxiv.2111.12921,
title = {Network regression and supervised centrality estimation},
author = {Junhui Cai and Dan Yang and Ran Chen and Wu Zhu and Haipeng Shen and Linda Zhao},
journal= {arXiv preprint arXiv:2111.12921},
year = {2025}
}