Book Content
chapters • 11h48m total length
1. Getting Started with Graph Learning
2. Graph Theory for Graph Neural Networks
3. Creating Node Representations with DeepWalk
4. Improving Embeddings with Biased Random Walks in Node2Vec
5. Including Node Features with Vanilla Neural Networks
6. Introducing Graph Convolutional Networks
7. Graph Attention Networks
8. Scaling Graph Neural Networks with GraphSAGE
9. Defining Expressiveness for Graph Classification
10. Predicting Links with Graph Neural Networks
11. Generating Graphs Using Graph Neural Networks
12. Learning from Heterogeneous Graphs
13. Temporal Graph Neural Networks
14. Explaining Graph Neural Networks
15. Forecasting Traffic Using A3T-GCN
16. Detecting Anomalies Using Heterogeneous Graph Neural Networks
17. Building a Recommender System Using LightGCN
18. Unlocking the Potential of Graph Neural Networks for Real-Word Applications
