Practical Assignments for Network Science

This repository contains practical assignments on network analysis. All assignments are presented as Jupyter notebooks, that can be done by writing code instead of the line

# YOUR CODE HERE

All notebooks contain test cells with assert statements that help you understand whether your code is correct.

Topics:

1. Introduction to Network Science
2. Power law and scale-free networks
3. Random graphs
4. Generative network models
5. Node centrality measures
6. Structural properties
7. Graph partitioning
8. Network communities
9. Epidemic models
10. Cascades and influence maximization
11. Node classification
12. Link prediction
13. Graph embeddings
14. Graph neural networks
15. Knowledge graphs

Here are also descriptions of competitions held among students to solve practical tasks on graphs:

1. Network generation
2. Marketing campaign
3. Link prediction

Lecture materials: http://leonidzhukov.net/hse/2023/networkscience/

Youtube channel with records of lectures: https://youtube.com/playlist?list=PLriUvS7IljvkGesFRuYjqRz4lKgodJgh2