Conformal Prediction for Network Assisted Regression

Robert Lunde, Liza Levina, Ji Zhu

Abstract:

An important problem in network analysis is predicting a node attribute using both
network covariates, such as graph embedding coordinates or local subgraph counts,
and conventional node covariates, such as demographic characteristics. While standard
regression methods that make use of both types of covariates may be used for prediction,
statistical inference is complicated by the fact that the nodal summary statistics are
often dependent in complex ways. We show that under a mild joint exchangeability
assumption, a network analog of conformal prediction achieves finite sample validity for
a wide range of network covariates. We also show that a form of asymptotic conditional
validity is achievable. The methods are illustrated on both simulated networks and a
citation network dataset.