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

TL;DR

This paper introduces a novel transfer learning method for node classification that leverages a high-dimensional graph convolutional regression model, offering theoretical guarantees and improved empirical performance over existing approaches.

Contribution

The paper proposes the GCR model and Trans-GCR transfer learning method, providing theoretical analysis and demonstrating superior empirical results with lower computational cost.

Findings

Theoretical guarantees for GCR in high-dimensional settings.

Trans-GCR outperforms existing methods in empirical tests.

Trans-GCR requires fewer hyperparameters and has lower computational cost.

Abstract

Node classification is a fundamental task, 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 Graph Convolutional Networks (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 high sensitivity to hyperparameter choices. To overcome these limitations, we propose a Graph Convolutional Multinomial Logistic Regression (GCR) model and a transfer learning method based on the GCR model, called Trans-GCR. We provide theoretical guarantees of the estimate obtained under GCR model in high-dimensional settings. Moreover, Trans-GCR demonstrates superior empirical performance, has a low computational cost, and requires fewer hyperparameters than existing methods.