MATH-448 / 5 credits

Teacher: Olhede Sofia Charlotta

Language: English

## Summary

A first course in statistical network analysis and applications.

## Keywords

• Basic description of a network and its generalizations (e.g. hypergraphs).
• Network examples from a practical point of view.
• Simple network summaries such as the degree distribution.
• Sparse and dense networks. Edge versus node models.
• Statistical implications of probabilistic properties of large networks.
• Erdos Renyi networks, simple models (configuration and stochastic block models).
• Sampling properties of network summaries.
• Fitting simple network models.
• Multilayer networks and directed networks
• Hypergraphs
• Exchangeability and probabilistic symmetries.
• Other topics as time permits.

## Required courses

probability and statistics

## Learning Outcomes

By the end of the course, the student must be able to:

• Recognize when a network model is appropriate
• Compute simple network summaries
• Assess / Evaluate parameters of basic network models from data
• Assess / Evaluate a range of network models and understand their propertie
• Assess / Evaluate the implications of model symmetries

## Teaching methods

Ex cathedra lectures and exercises

## Assessment methods

Final exam and assessed coursework that counts for 15%.

Dans le cas de l'art. 3 al. 5 du Règlement de section, l'enseignant décide de la forme de l'examen qu'il communique aux étudiants concernés.

## Supervision

 Office hours No Assistants Yes Forum No

No

## Bibliography

• R. Durrett: Random Graph Dynamics. Cambridge University Press 2007·
• E.D. Kolaczyk: Statistical Analysis of Network Data. Springer, 2009·
• Ibid Topics at the Frontier of Statistics and Network Analysis: (Re)Visiting The Foundations (SemStat Elements)·
• R. van der Hofstad. Random Graphs and Complex Networks Volume One, 2016 ·

## Notes/Handbook

available on moodle

## In the programs

• Semester: Fall
• Exam form: Written (winter session)
• Subject examined: Statistical analysis of network data
• Lecture: 2 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Semester: Fall
• Exam form: Written (winter session)
• Subject examined: Statistical analysis of network data
• Lecture: 2 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Semester: Fall
• Exam form: Written (winter session)
• Subject examined: Statistical analysis of network data
• Lecture: 2 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Semester: Fall
• Exam form: Written (winter session)
• Subject examined: Statistical analysis of network data
• Lecture: 2 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Semester: Fall
• Exam form: Written (winter session)
• Subject examined: Statistical analysis of network data
• Lecture: 2 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Semester: Fall
• Exam form: Written (winter session)
• Subject examined: Statistical analysis of network data
• Lecture: 2 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks

## Reference week

 Mo Tu We Th Fr 8-9 9-10 10-11 11-12 12-13 13-14 14-15 15-16 16-17 17-18 18-19 19-20 20-21 21-22

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