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Graph Convolutional Networks - GNN Paper Explained

Aleksa Gordić - The AI Epiphany via YouTube

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Overview

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This course provides an in-depth explanation of graph convolutional networks, covering their spectral motivation, vectorized formulation, graph embeddings, semi-supervised learning, variations, benchmarking, and limitations. It also compares GCNs with GINs and GATs and examines the effects of network depth.

Syllabus

Intro to GCNs
Graph Laplacian regularization methods
GCN method in-depth explanation
Vectorized form explanation
Spectral methods the motivation behind GCNs
Visualizing GCN hidden features t-SNE
Explanation of semi-supervised learning process
Graph embedding methods, results
Different variations of GCN
Speed benchmarking & limitations
Weisfeiler-Lehman perspective GCN vs GIN
GAT perspective, consequences of WL
GNN depth

Taught by

Aleksa Gordić - The AI Epiphany

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