Recent years have witnessed fast developments of graph neural networks (GNNs) that have
benefited myriads of graph analytic tasks and applications. In general, most GNNs depend …

Homophily principle,\ie {} nodes with the same labels or similar attributes are more likely to
be connected, has been commonly believed to be the main reason for the superiority of …

Graph representation learning has been a very active research area in recent years. The
goal of graph representation learning is to generate graph representation vectors that …

Unsupervised graph representation learning (UGRL) has drawn increasing research
attention and achieved promising results in several graph analytic tasks. Relying on the …

Abstract Graph Structure Learning (GSL) has recently garnered considerable attention due
to its ability to optimize both the parameters of Graph Neural Networks (GNNs) and the …

Abstract Graph Contrastive Learning (GCL) has drawn much research interest due to its
strong ability to capture both graph structure and node attribute information in a self …

Learning unbiased node representations under class-imbalanced graph data is challenging
due to interactions between adjacent nodes. Existing studies have in common that they …

Graph learning has become essential in various domains, including recommendation
systems and social network analysis. Graph Neural Networks (GNNs) have emerged as …

This paper studies the problem of conducting self-supervised learning for node
representation learning on graphs. Most existing self-supervised learning methods assume …

GCN is a widely-used representation learning method for capturing hidden features in graph
data. However, traditional GCNs suffer from the over-smoothing problem, hindering their …