A new computational fabric for Graph Neural Networks

“Graph Neural Networks (GNNs) typically align their computation graph with the structure of the input graph. But are graphs the right computational fabric for GNNs? A recent line of papers challenges this assumption by replacing graphs with more general objects coming from the field of algebraic topology, which offer multiple theoretical and computational advantages.”

A new Medium post by Michael Bronstein, Cristian Bodnar, and Fabrizio Frasca