Contrastive Pre-Training and Post-Tuning for Heterogeneous Graph Learning

Abstract

In recent years, the field of heterogeneous graph learning has garnered significant interest. Various efforts have been made towards learning heterogeneous graph representations, such as designing meta-paths to mine implicit graph knowledge or directly applying Graph Neural Networks (GNNs) for graph representation. However, these methods fail to fully capture available graph knowledge while ensuring scalability across diverse graph settings. In this paper, we address these challenges by introducing IEGraph, a heterogeneous Graph learning approach that capitalizes on both implicit and explicit graph knowledge. This encompasses two training stages: the implicit label-free stage and the explicit label-based stage, fostering comprehensive utilization of graph information. The label-free stage extracts implicit graph knowledge by constructing local and global training samples for contrastive pre-training, while the label-based stage further employs explicit labeled data to fine-tune the model. We carry out experiments on diverse heterogeneous graphs, and the results show that IEGraph achieves commendable performance compared to other state-of-the-art baselines.