A0447
Title: Network regression and supervised centrality estimation
Authors: Junhui Cai - University of Notre Dame (United States)
Ran Chen - Washington University in St. Louis (United States) [presenting]
Haipeng Shen - The University of Hong Kong (Hong Kong)
Dan Yang - University of Hong Kong (Hong Kong)
Linda Zhao - University of Pennsylvania (United States)
Wu Zhu - Tsinghua University (China)
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. A unified modeling framework is proposed to study the properties of centrality estimation and inference and the subsequent network regression analysis with noisy network observations. Furthermore, a supervised centrality estimation methodology is proposed, which aims to simultaneously estimate and infer both centrality and network effect. The advantages of the method compared with the two-stage method are showcased both theoretically and numerically via extensive simulations and a case study in predicting currency risk premiums from the global trade network.
