Graph Neural Networks through the lens of Differential Geometry and Algebraic Topology
Michael Bronstein
“Differential geometry and algebraic topology are not encountered very frequently in mainstream machine learning… tools from these fields can be used to reinterpret Graph Neural Networks and address some of their common plights in a principled way.”