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://towardsdatascience.com/do-we-need-deep-graph-neural-networks-be62d3ec5c59
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⌚️ Timetable:

00:00 Intro to GCNs
01:00 Graph Laplacian regularization methods
06:00 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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#graphconvolutionalnetwork #graphs #deeplearning

Видео Graph Convolutional Networks (GCN) | GNN Paper Explained канала Aleksa Gordić - The AI Epiphany