Abstract
This lecture overviews Graph Convolutional Networks (GCN) that have many applications in Deep Learning, Signal and Video Analysis, Network Theory, Web Science and Social Media Analytics. It covers the following topics in detail: Graph Convolutions. Empirical Risk Minimization with Graph Signals. Learning with Graph Convolutional Filters. Learning with Graph Perceptrons. GCN Types. GCN general architecture. Spectral Graph Convolution, Simple Spectral GCN, Spline GCN, LapGCN, ChebNet, CayleyNet. Spatial Graph Convolution, Simple Spatial GCN, GraphSage, GIN, MoNet, GAT, GatedGCN. GCN from scratch with numpy. Spatio-Temporal GCN.
Graph Isomorphism Networks.
Spatio-Temporal GCN.
Graph-Convolutional-Networks-v5.7.4-Summary