# Network regression and supervised centrality estimation

arXiv: https://arxiv.org/abs/2111.12921 · HTML: https://mlsanity.com/paper/2111.12921/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2021.

## Built on by (most cited first, 1 of 1)

- [Hypothesis testing on invariant subspaces of non-diagonalizable matrices with applications to network statistics](https://mlsanity.com/paper/2303.18233/) (2023)

## Abstract (arXiv)

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.