As the basic element of graph-structured data, node has been recognized as the main object of study in graph representation learning. A single node intuitively has multiple node-centered subgraphs from the whole graph (e.g., one person in a social network has multiple social circles based on his different relationships). We study this intuition under the framework of graph contrastive learning, and propose a multiple node-centered subgraphs contrastive representation learning method to learn node representation on graphs in a self-supervised way. Specifically, we carefully design a series of node-centered regional subgraphs of the central node. Then, the mutual information between different subgraphs of the same node is maximized by contrastive loss. Experiments on various real-world datasets and different downstream tasks demonstrate that our model has achieved state-of-the-art results.

用图对比学习的框架，我们提出了一种多节点中心子图对比表示学习方法，以自我监督的方式学习图中节点的表示，通过对中心节点的一系列节点中心区域子图进行精心设计，通过对同一节点的不同子图之间的互信息进行对比损失的最大化来实现。对各种真实世界数据集和不同下游任务的实验证明，我们的模型取得了最先进的结果。

基于多个节点中心子图的对比表示学习