Pre-training on large-scale heterogeneous graph

• Main Authors: JIANG, Xunqiang, JIA, Tianrui, FANG, Yuan, SHI, Chuan, LIN, Zhe, WANG, Hui
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: Heterogeneous graph; Self-supervised learning; Pre-training; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/6888; https://ink.library.smu.edu.sg/context/sis_research/article/7891/viewcontent/KDD21_PT_HGNN.pdf
• Institution: Singapore Management University

Graph neural networks (GNNs) emerge as the state-of-the-art representation learning methods on graphs and often rely on a large amount of labeled data to achieve satisfactory performance. Recently, in order to relieve the label scarcity issues, some works propose to pre-train GNNs in a self-supervised manner by distilling transferable knowledge from the unlabeled graph structures. Unfortunately, these pre-training frameworks mainly target at homogeneous graphs, while real interaction systems usually constitute large-scale heterogeneous graphs, containing different types of nodes and edges, which leads to new challenges on structure heterogeneity and scalability for graph pre-training. In this paper, we first study the problem of pre-training on large-scale heterogeneous graph and propose a novel pre-training GNN framework, named PT-HGNN. The proposed PT-HGNN designs both the node- and schema-level pre-training tasks to contrastively preserve heterogeneous semantic and structural properties as a form of transferable knowledge for various downstream tasks. In addition, a relationbased personalized PageRank is proposed to sparsify large-scale heterogeneous graph for efficient pre-training. Extensive experiments on one of the largest public heterogeneous graphs (OAG) demonstrate that our PT-HGNN significantly outperforms various state-of-the-art baselines.