Transfer Learning of Graph Neural Networks with Ego-graph Information Maximization

TL;DR

This paper introduces a theoretically grounded framework for transfer learning in GNNs, focusing on maximizing ego-graph information to improve transferability across different graph datasets.

Contribution

It proposes a novel ego-graph information maximization approach and provides theoretical analysis of transferability based on graph Laplacian differences.

Findings

Controlled experiments validate theoretical insights.

Consistent transfer performance on real-world datasets.

Promising results on large-scale knowledge graphs.

Abstract

Graph neural networks (GNNs) have achieved superior performance in various applications, but training dedicated GNNs can be costly for large-scale graphs. Some recent work started to study the pre-training of GNNs. However, none of them provide theoretical insights into the design of their frameworks, or clear requirements and guarantees towards their transferability. In this work, we establish a theoretically grounded and practically useful framework for the transfer learning of GNNs. Firstly, we propose a novel view towards the essential graph information and advocate the capturing of it as the goal of transferable GNN training, which motivates the design of EGI (Ego-Graph Information maximization) to analytically achieve this goal. Secondly, when node features are structure-relevant, we conduct an analysis of EGI transferability regarding the difference between the local graph Laplacians of the source and target graphs. We conduct controlled synthetic experiments to directly justify our theoretical conclusions. Comprehensive experiments on two real-world network datasets show consistent results in the analyzed setting of direct-transfering, while those on large-scale knowledge graphs show promising results in the more practical setting of transfering with fine-tuning.