Deep Learning on Graphs(3/3): Graph embedding

Deep Learning on Graphs(3/3): Graph embedding 0:00 Introduction 1:30 Graph embedding 2:43 How to represent graphs? 3:58 Why graph symmetries matter? 8:25 Invariant and equivariant functions 12:30 Message passing GNN 16:02 The many flavors of MGNN 20:00 Separating power 22:51 2-Weisfeiler-Lehman test 26:59 How powerful are MGNN 28:27 Empirical results 29:10 Graphs as higher order tensors 31:45 Invariant and equivariant linear operator 35:47 Invariant linear GNN 38:18 Folklore GNN https://dataflowr.github.io/website/