Conformal Prediction for Network-Assisted Regression

TL;DR

This paper introduces a network-adapted conformal prediction method that provides valid statistical inference for node attribute prediction in networks, accounting for complex dependencies among network covariates.

Contribution

It extends conformal prediction to network data under a mild exchangeability assumption, enabling valid inference for network-assisted regression.

Findings

Finite sample validity under exchangeability

Asymptotic conditional validity achieved

Effective on simulated and real citation networks

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.