Although existing graph self-supervised learning approaches have paid attention to the
directed nature of networks, they have often overlooked the ubiquitous scale-free attributes …

Graph self-supervised learning is now a go-to method for pre-training graph foundation
models, including graph neural networks, graph transformers, and more recent large …

Graph Masked Autoencoders (GMAEs) have emerged as a notable self-supervised learning
approach for graph-structured data. Existing GMAE models primarily focus on reconstructing …

Recently, generative graph models have shown promising results in learning graph
representations through self-supervised methods. However, most existing generative graph …

Graph self-supervised learning has gained significant attention recently. However, many
existing approaches heavily depend on perturbations, and inappropriate perturbations may …