Graph SAGE - Inductive Representation Learning on Large Graphs | GNN Paper Explained
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In this video, I do a deep dive into the Graph SAGE paper!
The first paper that started pushing the usage of GNNs for super large graphs.
You'll learn about:
✔️All the nitty-gritty details behind Graph SAGE
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✅ Graph SAGE paper: https://arxiv.org/abs/1706.02216
✅ Chris Olah on LSTMs: https://colah.github.io/posts/2015-08...
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⌚️ Timetable:
00:00 Intro
00:38 Problems with previous methods
04:30 High-level overview of the method
06:10 Some notes on the related work
07:13 Pseudo-code explanation
12:03 How do we train Graph SAGE?
15:40 Note on the neighborhood function
17:40 Aggregator functions
23:30 Results
28:00 Expressiveness of Graph SAGE
30:10 Mini-batch version
35:30 Problems with graph embedding methods (drift)
40:30 Comparison with GCN and GAT
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#graphsage #gnns #graphtheory