Diffusion models have emerged as a powerful new family of deep generative models with
record-breaking performance in many applications, including image synthesis, video …

Deep learning on graphs has attracted significant interests recently. However, most of the
works have focused on (semi-) supervised learning, resulting in shortcomings including …

Inspired by the recent success of self-supervised methods applied on images, self-
supervised learning on graph structured data has seen rapid growth especially centered on …

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 …

Graph contrastive learning (GCL) improves graph representation learning, leading to SOTA
on various downstream tasks. The graph augmentation step is a vital but scarcely studied …

Graph contrastive learning (GCL) alleviates the heavy reliance on label information for
graph representation learning (GRL) via self-supervised learning schemes. The core idea is …

Although augmentations (eg, perturbation of graph edges, image crops) boost the efficiency
of Contrastive Learning (CL), feature level augmentation is another plausible …

Contrastive learning has recently attracted plenty of attention in deep graph clustering due to
its promising performance. However, complicated data augmentations and time-consuming …

Data augmentation has recently seen increased interest in graph machine learning given its
demonstrated ability to improve model performance and generalization by added training …

Knowledge graph embedding (KGE) aims at learning powerful representations to benefit
various artificial intelligence applications. Meanwhile, contrastive learning has been widely …