Covers: theory of Graph Neural Networks

- How can i learn a lot about graphs?

The videos and some slides from the workshop at Stanford are posted here. You can check out the topics you're interested in

Standford

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Contributors

- Objectives
- This recipe provides an overview of Graph Neural Networks and various applications they enable
- Potential Use Cases
- relationship preiction, recommender systems, atribute classification, drug discovery, physics based simulations
- Who is This For ?
- INTERMEDIATEML Developers and Data Scientists familiar with basics of ML

Click on each of the following **annotated items** to see details.

Resources5/30

VIDEO 1. Introduction to Graphs

- What are Graph Neural Networks?

2 minutes

RECIPE 2. Graph Data, Representations, and Tasks

47 minutes

RECIPE 3. Node Classification using Graph Neural Networks

73 minutes

RECIPE 4. Link Prediction using Graph Neural Networks

60 minutes

REPO 5. Heterogeneous Graphs

- How to deal with Heterogeneous nodes on graphs?

20 minutes

VIDEO 6. CS224W: Machine Learning with Graphs Playlist

- What can you do with ML on graphs?

3 hours

BOOK_CHAPTER 7. Graph Neural Networks in Action

10 minutes

ARTICLE 8. A Gentle Introduction to Graph Neural Networks

- What are GNNs used for?

10 minutes

ARTICLE 9. Understanding Convolutions on Graphs

- How can convolutions be used on graph data?

10 minutes

VIDEO 10. A Literature Review on Graph Neural Networks

10 minutes

VIDEO 11. Overview of Machine Learning for Knowledge Graphs

10 minutes

VIDEO 12. TGN: Temporal Graph Networks for Deep Learning on Dynamic Graphs

10 minutes

VIDEO 13. Inductive Representation Learning on Temporal Graphs

10 minutes

VIDEO 14. [GAT] Graph Attention Networks

10 minutes

VIDEO 15. [GATA] Learning Dynamic Belief Graphs to Generalize on Text-Based Games

10 minutes

VIDEO 16. Learning to Represent Programs with Graphs

10 minutes

VIDEO 17. SELFIES: A 100% robust representation of semantically constrained Graphs, for deep generative models

10 minutes

VIDEO 18. Junction Tree Variational Autoencoder for Molecular Graph Generation

10 minutes

VIDEO 19. [hgraph2graph] Hierarchical Generation of Molecular Graphs using Structural Motifs

10 minutes

VIDEO 20. Learning Mesh-Based Simulation with Graph Networks

10 minutes

VIDEO 21. Learning to Simulate Complex Physics with Graph Networks

10 minutes

VIDEO 22. Meta-Graph: Few-Shot Link Prediction Using Meta-Learning

10 minutes

VIDEO 23. Memory-Based Graph Networks

10 minutes

VIDEO 24. Principal Neighbourhood Aggregation for Graph Nets

10 minutes

VIDEO 25. Learning Discrete Structures for Graph Neural Networks

10 minutes

VIDEO 26. Representation Learning of Histopathology Images using Graph Neural Networks

10 minutes

VIDEO 27. Efficient Neighborhood-based Interaction Model for Recommendation on Heterogeneous Graph

10 minutes

VIDEO 28. Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformer

10 minutes

VIDEO 29. Nodes, Edges and Properties; Graph Analysis Intro for ML Newcomers

10 minutes

OTHER 30. Stanford Graph Learning Workshop 2021

- How can i learn a lot about graphs?

3 hours

I don't know how I can update the recipes but I think these articles published today should be part of this recipe, at least the Introduction one.
1. https://distill.pub/2021/gnn-intro/
2. https://distill.pub/2021/understanding-gnns/

Ashok Tak. funnily enough I marked these 2 to add them to the recipe. I'll add them later today. if there are other resources you think we should add ping me and I'll add you as a collaborator so that you can add those directly