  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.
