  This paper focuses on learning representation on the whole graph level in an
unsupervised manner. Learning graph-level representation plays an important
role in a variety of real-world issues such as molecule property prediction,
protein structure feature extraction, and social network analysis. The
mainstream method is utilizing contrastive learning to facilitate graph feature
extraction, known as Graph Contrastive Learning (GCL). GCL, although effective,
suffers from some complications in contrastive learning, such as the effect of
false negative pairs. Moreover, augmentation strategies in GCL are weakly
adaptive to diverse graph datasets. Motivated by these problems, we propose a
novel framework called Structure Knowledge Refinement (SKR) which uses data
structure to determine the probability of whether a pair is positive or
negative. Meanwhile, we propose an augmentation strategy that naturally
preserves the semantic meaning of the original data and is compatible with our
SKR framework. Furthermore, we illustrate the effectiveness of our SKR
framework through intuition and experiments. The experimental results on the
tasks of graph-level classification demonstrate that our SKR framework is
superior to most state-of-the-art baselines.
