In the last decade, deep learning has reinvigorated the machine learning field. It has solved
many problems in computer vision, speech recognition, natural language processing, and …

Self-supervised learning on graph-structured data has drawn recent interest for learning
generalizable, transferable and robust representations from unlabeled graphs. Among …

Graph contrastive learning (GCL) has emerged as a dominant technique for graph
representation learning which maximizes the mutual information between paired graph …

Generalizable, transferrable, and robust representation learning on graph-structured data
remains a challenge for current graph neural networks (GNNs). Unlike what has been …

There has been a lot of activity in graph representation learning in recent years. Graph
representation learning aims to produce graph representation vectors to represent the …

We introduce a self-supervised approach for learning node and graph level representations
by contrasting structural views of graphs. We show that unlike visual representation learning …

This paper studies learning the representations of whole graphs in both unsupervised and
semi-supervised scenarios. Graph-level representations are critical in a variety of real-world …

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 …

Contrastive learning has been widely applied to graph representation learning, where the
view generators play a vital role in generating effective contrastive samples. Most of the …

Self-supervised learning of graph neural networks (GNN) is in great need because of the
widespread label scarcity issue in real-world graph/network data. Graph contrastive learning …