Graph Neural Networks Implementation in Python

This course explains graph representations and core graph neural network concepts, then applies them in Python with NetworkX and PyG. Practical exercises cover node embeddings, message passing, node classification, visualization, and GNN explanation.

Syllabus

- Video Starts
- Video Introduction
- Tutorial Content in Part2
- Graph Representations Techniques
- Adjacency Matrix
- Incidence Matrix
- Degree Matrix
- Laplacian Matrix
- Creating Graph with NetworkX Jupyter notebook
- Graph Visualization with Node classes Jupyter notebook
- Graph Visualization with Node and Edge Labels Jupyter notebook
- Nodes Adjacency List Jupyter notebook
- Bag of Nodes
- Graph Walking Jupyter notebook
- GNN Concepts
- Role of Laplacian Matrix
- Convolution in Images
- Graph vs 2D fixed data types i.e. images, text
- Convolution on Graphs, how?
- Graph Feature Matrix
- Applying Convolution in Graphs
- Node Embeddings
- Message Passing in GNN
- Advantages of Node Embeddings
- GNN Use Cases
- Handling data in PyG Jupyter notebook
- GNN Experiment for Node grouping Jupyter notebook
- Node assignment to proper class Jupyter notebook
- GNN Model visualization with Netron
- Node classification using GNN in PyG
- Graph tSNE Visualization
- GNN Explainer
- Recap