Further reading
If you want to learn more about the topics covered in this chapter, I recommend the following resources:
- An LP analysis with topological metrics and
scikit-learnis presented in the book Graph Algorithms by M. Needham and A. Hodler, O’Reilly (Chapter 8, Building a GDS Pipeline for Node Classification Model Training). - This paper introducing LP problems: Link Prediction in Complex Networks: A Survey by L. Lu and T. Zhou: https://arxiv.org/abs/1010.0725.
- Some more complex LP examples:
- LP on heterogeneous graphs:
- Using PyG: Link Prediction on Heterogeneous Graphs with PyG by J. Eric Lenssen and M. Fey: https://medium.com/@pytorch_geometric/link-prediction-on-heterogeneous-graphs-with-pyg-6d5c29677c70
- Using GraphSAGE for recommendations in heterogeneous graphs: Graph Neural Networks: Link Prediction (Part II) by L. Faik: https://medium.com/data-from-the-trenches/graphical-neural-networks-link-prediction-part-ii-c60f6d97fd97
- Multi-class link prediction...
- LP on heterogeneous graphs:
