Notation

CS224w_2021_fall

PyTorch_Geometric

1. Introduction: Hands-on Graph Neural Networks
- Implementing Graph Neural Networks
  - Embedding the Karate Club Network
  - Training on the Karate Club Network
- Conclusion
2. Node Classification with Graph Neural Networks
3. Graph Classification with Graph Neural Networks
4. Scaling Graph Neural Networks
- Conclusion
5. Point Cloud Classification with Graph Neural Networks
6. Explaining GNN Model Predictions using Captum
7. Customizing Aggregations within Message Passing with torch_geometric.nn.aggr
8. Node Classification with W&B

Pyg_Tutorial_Project

Tutorial 1: Introduction
- Load the dataset
  - Dataset properties
  - Dataset shapes
Tutorial 2: PyTorch basics
Tutorial 3: GAT implementation
Tutorial 4: Convolutional Layers - Spectral methods
Tutorial 5: Aggregation
- Message Passing Class
Tutorial 6: GAE & VGAE
- Graph AutoEncoder GAE
- Use Tensorboard
  - Import tensorboard
    - Installation: (if needed) “pip install tensorboard”
  - Graph Variational AutoEncoder (GVAE)
Tutorial 7: ARGA & ARVGA
Tutorial 9_1: Recurrent GNNs
- Graph Neural Network Model
  - Gated Graph Neural Network
Tutorial 9_2: Recurrent GNNs
- Graph Neural Network Model
- Gated Graph Neural Network
Tutorial 11: DeepWalk and node2vec - Implementation details
Tutorial 12: GAE for link prediction
- Simple autoencoder model
Tutorial 13: Node2Vec for link prediction
Tutorial 14: Data Handling in PyG (Part 1)
- Data
  - Batch
  - Cluster
  - Sampler
  - Datasets
Tutorial 15: Data Handling in PyG (Part 2)
- Custom PyG dataset
  - Open Graph Benchmark datasets
  - Benchmarking Graph Neural Networks Datasets

Category_theory

GDL_book

Papers

AMMI_ex

Notes

rosicast

Ex 1¶

lec_3_ex_1

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