Rosicast’s documentation¶
This site is inspired by the ml-glossary, Stanford cs224w lectures, and many others. There could be the same contents at the begining.
Math
Levels
Jax related
ogbn-proteins
ogbn-arxiv
CS224w_2021_fall
PyTorch_Geometric
- 1. Introduction: Hands-on Graph Neural Networks
- 2. Node Classification with Graph Neural Networks
- 3. Graph Classification with Graph Neural Networks
- 4. Scaling Graph Neural Networks
- 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
- Tutorial 2: PyTorch basics
- Tutorial 3: GAT implementation
- Tutorial 4: Convolutional Layers - Spectral methods
- Tutorial 5: Aggregation
- Tutorial 6: GAE & VGAE
- Tutorial 7: ARGA & ARVGA
- Tutorial 9_1: Recurrent GNNs
- Tutorial 9_2: Recurrent GNNs
- Tutorial 11: DeepWalk and node2vec - Implementation details
- Tutorial 12: GAE for link prediction
- Tutorial 13: Node2Vec for link prediction
- Tutorial 14: Data Handling in PyG (Part 1)
- Tutorial 15: Data Handling in PyG (Part 2)
Representation
Resources
TodoList
project guide
CS 224W Project (2021 Fall) Tutorials and Case Studies for Applying Graph ML to Real-World Problems