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GNN depth
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Classroom Contents
Graph Convolutional Networks - GNN Paper Explained
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- 1 Intro to GCNs
- 2 Graph Laplacian regularization methods
- 3 GCN method in-depth explanation
- 4 Vectorized form explanation
- 5 Spectral methods the motivation behind GCNs
- 6 Visualizing GCN hidden features t-SNE
- 7 Explanation of semi-supervised learning process
- 8 Graph embedding methods, results
- 9 Different variations of GCN
- 10 Speed benchmarking & limitations
- 11 Weisfeiler-Lehman perspective GCN vs GIN
- 12 GAT perspective, consequences of WL
- 13 GNN depth
