Topology-Informed Pre-training of Graph Neural Networks

Abstract

Pre-training has emerged as a dominant paradigm in graph representation learning to address data scarcity and generalization challenges. The majority of existing methods primarily focus on refining fine-tuning and prompting techniques to extract information from pre-trained models. However, the effectiveness of these approaches is contingent upon the quality of the pre-trained knowledge (i.e., latent representations). Inspired by the recent success in topological representation learning, we propose a novel pre-training strategy to capture and learn topological information of graphs. The key to the success of our strategy is to pre-train expressive Graph Neural Networks (GNNs) at the levels of individual nodes while accounting for the key topological characteristics of a graph so that GNNs become sufficiently powerful to effectively encode input graph information. The proposed model is designed to be seamlessly integrated with various downstream graph representation learning tasks. Data and code are available at https://github.com/pyliang-graph/Topology-Informed-Pre-training-of-Graph-Neural-Networks.