Social network analysis 1000-2M25ASI

1. Introduction to social network analysis:
- basic concepts of social network analysis,
- network generation models - Erdos-Renyi, Watts-Strogatz, Barabasi-Albert, Newman configuration, Steiner trees,
- small-world property, degree distributions, scale-freeness.

2. Network resilience:
- strategic attack vs random error,
- resilience in terms of connectivity and distance,
- network flow and network cuts, resilience curves.

3. The computational complexity of network problems:
- reductions using network problems,
- approximation algorithms,
- the hardness of approximation.

4. Centrality measures:
- degree, closeness, betweenness,
- eigenvector, spectral analysis, and random walks,
- Pagerank, Katz, Bonacich,
- game-theoretic centralities.

5. Community detection and group centrality measures:
- homophily in networks,
- community structure and the modularity measure, community detection algorithms,
- group centrality measures.

6. Link prediction algorithms:
- local and global similarity indices,
- evaluation of link prediction.

7. Influence and compartmental models, source detection algorithms:
- independent cascade and linear threshold models, influence maximization problem,
- SI, SIS, SIR, SEIR compartmental models,
- source detection algorithms.

8. Multilayer and temporal networks:
- multilayer networks, local and global centrality measures,
- temporal networks, latency as the measure of distance.

9. Graph neural networks:
- machine learning on network data,
- node and edge embedding,
- training graph neural networks.