In this paper, we introduce a self-supervised learning method to enhance the graph-level
representations with the help of a set of subgraphs. For this purpose, we propose a universal …

Abstract Information theory has shown a notable performance in the field of computer vision
(CV) and natural language processing (NLP), therefore, many works start to learn better …

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 …

We introduce a conceptually simple yet effective model for self-supervised representation
learning with graph data. It follows the previous methods that generate two views of an input …

Graph representation is a critical technology in the field of knowledge engineering and
knowledge-based applications since most knowledge bases are represented in the graph …

Graph contrastive learning, which to date has always been guided by node features and
fixed-intrinsic structures, has become a prominent technique for unsupervised graph …

The recently developed unsupervised graph representation learning approaches apply
contrastive learning into graph-structured data and achieve promising performance …

A variety of graph neural networks (GNNs) frameworks for representation learning on graphs
have been recently developed. These frameworks rely on aggregation and ITERATION …

Contrastive learning has made breakthrough advancements in graph representation
learning, which encourages the representation of positive samples to be close and those of …

Self-supervised learning for graph neural networks has attracted considerable attention and
shows notable successes in graph representation learning. However, the formation of a real …