Although link prediction on graphs has achieved great success with the development of graph neural networks (GNNs), the potential robustness under the edge noise is still less investigated.

Simultaneously, convex formulations for training machine learning tasks have been replaced by nonconvex representations such as neural networks.

These papers focus on building the symmetries into the models by modifying the architecture.

The pursuit of realistic image synthesis at arbitrary views, given only a single source image, has long been a cornerstone challenge in computer vision and graphics.