Theory of Graph Neural Networks: Representation and Learning

Theory of Graph Neural Networks: Representation and Learning

International Mathematical Union via YouTube Direct link

Intro

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1 of 22

Intro

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Theory of Graph Neural Networks: Representation and Learning

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  1. 1 Intro
  2. 2 Machine Learning in one picture
  3. 3 Machine Learning with Graph Data: Applicat
  4. 4 Outline
  5. 5 GNNS: Origins and Relations
  6. 6 Message Passing Graph Neural Networks
  7. 7 Message Passing for Node Embedding
  8. 8 Fully connected Neural Network (FNN)
  9. 9 Message Passing Tree
  10. 10 Function Approximation and Graph Distincti
  11. 11 Color refinement/Weisfeiler-Leman algorith
  12. 12 Improving discriminative power
  13. 13 Node IDs and Local Algorithms
  14. 14 The challenge with generalization
  15. 15 Bounding the generalization gap
  16. 16 Neural Tangent Kernel
  17. 17 Computational structure
  18. 18 Algorithmic Alignment
  19. 19 Big picture: when may extrapolation "work"?
  20. 20 Extrapolation in fully connected ReLU netwo
  21. 21 Implications for the full GNN
  22. 22 Open Questions...

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