Graph Convolutional Networks (GCN) | GNN Paper Explained
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In this video I do a deep dive into the graph convolutional networks paper!
It's currently the most cited paper in the GNN literature at the time of making this video.
You'll learn about:
✔️ All the nitty-gritty details behind GCN
✔️ 3 different perspectives (spectral, WL, MPNN)
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✅ GCN paper: https://arxiv.org/abs/1609.02907
✅ T.Kipf's awesome website: https://tkipf.github.io/graph-convolutional-networks/
✅ M.Bronstein's blog on GNN depth: https://towardsdatascienc…
Watch on YouTube ↗
(saves to browser)
Chapters (13)
Intro to GCNs
1:00
Graph Laplacian regularization methods
6:00
GCN method (in-depth explanation)
12:40
Vectorized form explanation
17:05
Spectral methods (the motivation behind GCNs)
29:20
Visualizing GCN hidden features (t-SNE)
30:17
Explanation of semi-supervised learning process
32:07
Graph embedding methods, results
34:12
Different variations of GCN
36:30
Speed benchmarking & limitations
39:30
Weisfeiler-Lehman perspective (GCN vs GIN)
44:25
GAT perspective, consequences of WL
46:45
GNN depth
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