Generative self-supervised learning (SSL), especially masked autoencoders, has become
one of the most exciting learning paradigms and has shown great potential in handling …

Self-supervised learning (SSL) has been extensively explored in recent years. Particularly,
generative SSL has seen emerging success in natural language processing and other …

Graph self-supervised learning (SSL), including contrastive and generative approaches,
offers great potential to address the fundamental challenge of label scarcity in real-world …

Self-supervised learning with masked autoencoders has recently gained popularity for its
ability to produce effective image or textual representations, which can be applied to various …

The last years have witnessed the emergence of a promising self-supervised learning
strategy, referred to as masked autoencoding. However, there is a lack of theoretical …

We introduce a novel masked graph autoencoder (MGAE) framework to perform effective
learning on graph structure data. Taking insights from self-supervised learning, we randomly …

Self-supervised learning (SSL) has been demonstrated to be effective in pre-training models
that can be generalized to various downstream tasks. Graph Autoencoder (GAE), an …

Self-supervised learning (SSL) for graph neural networks (GNNs) has attracted increasing
attention from the graph machine learning community in recent years, owing to its capability …

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

Generative graph self-supervised learning (SSL) aims to learn node representations by
reconstructing the input graph data. However, most existing methods focus on unsupervised …