Post #277

Latent graph neural networks: Manifold learning 2.0? One of the hot topics of this year is construction of a graph from unstructured data (e.g. 3d points or images). In a new post Michael Bronstein discusses existing approaches to latent graph learning and suggests that using GNN both to learn the structure of the graph and to solve the downstream tasks can be a better alternative than a de-coupled approach. This is indeed an exciting and active area of research with open problems and known applications to NLP, physics, and biology.