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Insights Graph Neural Networks as Neural Diffusion PDEs

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

Insights Provably expressive graph neural networks

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

Insights ICLR Invited Talk on Geometric Deep Learning

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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

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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

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A new neural network architecture for dynamic graphs

Insights Graph ML at Twitter

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How we do Graph Machine Learning at Twitter