- Payal Chandak (chandak@mit.edu)
- Haoxin Li (haoxin_li@hsph.harvard.edu)
- Min Jean Cho (min_jean_cho@brown.edu)
- Pavlin Policar (pavlin.policar@fri.uni-lj.si)
- Mert Erden (mert.erden@tufts.edu)
- Steffan Paul (steffanpaul@g.harvard.edu)
- Marinka Zitnik (marinka@hms.harvard.edu)
Graph machine learning provides a powerful toolbox to learn representations from any arbitrary graph structure and use learned representations for a variety of downstream tasks. These tutorials aim to:
- Introduce the concept of graph neural networks (GNNs).
- Discuss the theoretical motivation behind different GNN architectures.
- Provide implementations of these architectures.
- Apply the architectures to key prediction problems on interconnected data in science and medicine.
- Provide end-to-end real-world examples of graph machine learning.
Recent versions of NumPy, PyTorch, PyTorch Geometric and Jupyter are required.
All the required packages can be installed using the following commands:
git clone https://github.com/mims-harvard/graphml-tutorials.gitcd graphml-tutorialschmod +x install.sh && ./install.shconda activate graphml_venv
Pull requests are welcome.