Week 13

$$\gdef \sam #1 {\mathrm{softargmax}(#1)}$$ $$\gdef \vect #1 {\boldsymbol{#1}} $$ $$\gdef \matr #1 {\boldsymbol{#1}} $$ $$\gdef \E {\mathbb{E}} $$ $$\gdef \V {\mathbb{V}} $$ $$\gdef \R {\mathbb{R}} $$ $$\gdef \N {\mathbb{N}} $$ $$\gdef \relu #1 {\texttt{ReLU}(#1)} $$ $$\gdef \D {\,\mathrm{d}} $$ $$\gdef \deriv #1 #2 {\frac{\D #1}{\D #2}}$$ $$\gdef \pd #1 #2 {\frac{\partial #1}{\partial #2}}$$ $$\gdef \set #1 {\left\lbrace #1 \right\rbrace} $$

Lecture part A

In this section, we discuss the architecture and convolution of traditional convolutional neural networks. Then we extend to the graph domain. We understand the characteristics of graph and define the graph convolution. Finally, we introduce spectral graph convolutional neural networks and discuss how to perform spectral convolution.

Lecture part B

This section covers the complete spectrum of Graph Convolutional Networks (GCNs), starting with the implementation of Spectral Convolution through Spectral Networks. It then provides insights on applicability of the other convolutional definition of Template Matching to graphs, leading to Spatial networks. Various architectures employing the two approaches are detailed out with their corresponding pros & cons, experiments, benchmarks and applications.


In this section, we introduce Graph Convolutional Network (GCN) which is one type of architecture that utilizes the structure of data. Actually, the concept of GCNs is closely related to self-attention. After understanding the general notation, representation and equations of GCN, we delve into the theory and code of a specific type of GCN known as Residual Gated GCN.