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Michael Bronstein


Head of Graph Learning Research

Insights Graph Neural Networks as Neural Diffusion PDEs

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

Insights Provably expressive graph neural networks

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

Insights ICLR Invited Talk on Geometric Deep Learning

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)

Insights Simple scalable graph neural networks

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

Insights Deep learning on dynamic graphs

A new neural network architecture for dynamic graphs

Insights Graph ML at Twitter

How we do Graph Machine Learning at Twitter