Over-squashing is a common plight of Graph Neural Networks. In this post, we discuss how this phenomenon can be understood and remedied through the concept of Ricci curvature.

In this post, we show how tools from the fields of differential geometry and algebraic topology can be used to reinterpret GNNs and address some of their common plights in a principled way.

In this post, we will discuss our recent work on neural graph diffusion networks.

Twitter describes how to design local and computationally efficient provably powerful graph neural networks.

This blog post is based on Michael Bronstein’s keynote talk at ICLR 2021 and the paper M. M. Bronstein et al., Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges (2021)

In this post, we describe a graph neural network architecture (SIGN) that is of simple implementation and that works on very large graphs.

A new neural network architecture for dynamic graphs