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.
VIDEO 1. Introduction to Graphs
What are Graph Neural Networks?
RECIPE 2. Graph Data, Representations, and Tasks
RECIPE 3. Node Classification using Graph Neural Networks
RECIPE 4. Link Prediction using Graph Neural Networks
REPO 5. Heterogeneous Graphs
How to deal with Heterogeneous nodes on graphs?
VIDEO 6. CS224W: Machine Learning with Graphs Playlist
What can you do with ML on graphs?
BOOK_CHAPTER 7. Graph Neural Networks in Action
ARTICLE 8. A Gentle Introduction to Graph Neural Networks
What are GNNs used for?
ARTICLE 9. Understanding Convolutions on Graphs
How can convolutions be used on graph data?
VIDEO 10. A Literature Review on Graph Neural Networks
VIDEO 11. Overview of Machine Learning for Knowledge Graphs
VIDEO 12. TGN: Temporal Graph Networks for Deep Learning on Dynamic Graphs
VIDEO 13. Inductive Representation Learning on Temporal Graphs
VIDEO 14. [GAT] Graph Attention Networks
VIDEO 15. [GATA] Learning Dynamic Belief Graphs to Generalize on Text-Based Games
VIDEO 16. Learning to Represent Programs with Graphs
VIDEO 17. SELFIES: A 100% robust representation of semantically constrained Graphs, for deep generative models
VIDEO 18. Junction Tree Variational Autoencoder for Molecular Graph Generation
VIDEO 19. [hgraph2graph] Hierarchical Generation of Molecular Graphs using Structural Motifs
VIDEO 20. Learning Mesh-Based Simulation with Graph Networks
VIDEO 21. Learning to Simulate Complex Physics with Graph Networks
VIDEO 22. Meta-Graph: Few-Shot Link Prediction Using Meta-Learning
VIDEO 23. Memory-Based Graph Networks
VIDEO 24. Principal Neighbourhood Aggregation for Graph Nets
VIDEO 25. Learning Discrete Structures for Graph Neural Networks
VIDEO 26. Representation Learning of Histopathology Images using Graph Neural Networks
VIDEO 27. Efficient Neighborhood-based Interaction Model for Recommendation on Heterogeneous Graph
VIDEO 28. Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformer
VIDEO 29. Nodes, Edges and Properties; Graph Analysis Intro for ML Newcomers
OTHER 30. Stanford Graph Learning Workshop 2021
How can i learn a lot about graphs?
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.
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