Prediction models with graph kernel regularization for network data.

ABSTRACT: Traditional regression methods typically consider only covariate information and assume that the observations are mutually independent samples. However, samples usually come from individuals connected by a network in many modern applications. We present a risk minimization formulation for learning from both covariates and network structure in the context of graph kernel regularization. The formulation involves a loss function with a penalty term. This penalty can be used not only to encourage similarity between linked nodes but also lead to improvement over traditional regression models. Furthermore, the penalty can be used with many loss-based predictive methods, such as linear regression with squared loss and logistic regression with log-likelihood loss. Simulations to evaluate the perfo

SUBMITTER: Liu J 

PROVIDER: S-EPMC10071950 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature