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.

翻译：作为图结构数据的基本元素，节点已被公认为图表示学习的主要研究对象。单个节点直观上可对应全图中的多个中心子图（例如，社交网络中的个体基于不同社会关系形成多个社交圈子）。我们基于图对比学习框架研究这一直觉，并提出一种基于多节点中心子图的对比表示学习方法，以实现自监督方式下的节点表示学习。具体而言，我们精心设计中心节点的一系列邻域子图，并通过对比损失最大化同一节点不同子图间的互信息。在多个真实数据集及不同下游任务上的实验表明，我们的模型取得了最优结果。