Transfer Learning Under High-Dimensional Graph Convolutional Regression for Node Classification

Music City Center 

Node classification is a fundamental task for network data, but obtaining node classification labels can be challenging and expensive in many real-world scenarios. Transfer learning has emerged as a promising solution to address this challenge by leveraging knowledge from source domains to enhance learning in a target domain. Existing transfer learning methods for node classification primarily focus on integrating GCNs with various transfer learning techniques. While these approaches have shown promising results, they often suffer from a lack of theoretical guarantees, restrictive conditions, and are highly sensitive to hyperparameter tuning. To address these limitations, we introduce the Graph Convolutional Multinomial Logistic Lasso Regression (GCR) model, a simplified version of GCN, and propose a novel transfer learning method Trans-GCR with theoretical guarantees. Trans-GCR demonstrates superior empirical performance, has a low computational cost, and requires fewer hyperparameters than existing methods. We also illustrate how Trans-GCR enhances Alzheimer's Disease risk assessment in smaller target cohorts by transferring knowledge from larger, well-characterized biobank.

Transfer learning

Graph convolutions

High-dimensional

Node classification 

Section on Statistical Learning and Data Science