This paper studies unsupervised/self-supervised whole-graph representation
learning, which is critical in many tasks such as molecule properties
prediction in drug and material discovery. Existing methods mainly focus on
preserving the local similarity structure between different graph instances but
fail to discover the global semantic structure of the entire data set. In this
paper, we propose a unified framework called Local-instance and Global-semantic
Learning (GraphLoG) for self-supervised whole-graph representation learning.
Specifically, besides preserving the local similarities, GraphLoG introduces
the hierarchical prototypes to capture the global semantic clusters. An
efficient online expectation-maximization (EM) algorithm is further developed
for learning the model. We evaluate GraphLoG by pre-training it on massive
unlabeled graphs followed by fine-tuning on downstream tasks. Extensive
experiments on both chemical and biological benchmark data sets demonstrate the
effectiveness of the proposed approach.

该论文提出了一种统一框架，名为 GraphLoG，用于自监督整图表示学习，其除保留本地相似性外，还引入了分层原型来捕捉全局语义簇，进一步发展了一个高效的在线期望最大化算法来学习该模型，并在化学和生物基准数据集上进行了广泛实验，证明所提出的方法的有效性。