Graph representation learning has emerged as a powerful technique for
addressing real-world problems. Various downstream graph learning tasks have
benefited from its recent developments, such as node classification, similarity
search, and graph classification. However, prior arts on graph representation
learning focus on domain specific problems and train a dedicated model for each
graph dataset, which is usually non-transferable to out-of-domain data.
Inspired by the recent advances in pre-training from natural language
processing and computer vision, we design Graph Contrastive Coding (GCC) -- a
self-supervised graph neural network pre-training framework -- to capture the
universal network topological properties across multiple networks. We design
GCC's pre-training task as subgraph instance discrimination in and across
networks and leverage contrastive learning to empower graph neural networks to
learn the intrinsic and transferable structural representations. We conduct
extensive experiments on three graph learning tasks and ten graph datasets. The
results show that GCC pre-trained on a collection of diverse datasets can
achieve competitive or better performance to its task-specific and
trained-from-scratch counterparts. This suggests that the pre-training and
fine-tuning paradigm presents great potential for graph representation
learning.

本论文提出了一个自监督的图神经网络预训练框架 Graph Contrastive Coding（GCC），通过子图实例区分学习网络的建构，利用对比学习增强图神经网络的内在和可迁移的结构表现，并在十个图数据集上展开实验，结果表明，GCC 对于包括节点分类、相似性搜索和图分类等三个图学习任务，展现出与特定任务和从零开始训练的对应模型相竞争的性能，同时显示了预训练和微调范式在图表示学习中有很大的潜力。