Rethinking GNNs
Graph Neural Networks through the lens of Differential Geometry and Algebraic Topology
Differential geometry and algebraic topology are not encountered very frequently in mainstream machine learning. In this series of posts, I show how tools from these fields can be used to reinterpret Graph Neural Networks and address some of their common plights in a principled way.
Published in
11 min readNov 18, 2021
