Statistical analysis of network data
Summary
A first course in statistical network analysis and applications.
Content
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.
Learning Prerequisites
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 |
Resources
Virtual desktop infrastructure (VDI)
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 ·
- M. Newman: Networks: An Introduction, OUP 2010.
- Probabilistic Foundations of Statistical Network AnalysisH CraneChapman and Hall/CRC
Ressources en bibliothèque
- Statistical Analysis of Network Data / Kolaczyk
- Random Graph Dynamics / Durrett
- Topics at the Frontier of Statistics and Network Analysis / Kolaczyk
- Random Graphs and Complex Networks / van der Hofstad
- Networks / Newman
- Probabilistic Foundations of Statistical Network Analysis / Crane
Notes/Handbook
available on moodle
Moodle Link
Videos
In the programs
- Semester: Fall
- Exam form: Written (winter session)
- Subject examined: Statistical analysis of network data
- Courses: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Fall
- Exam form: Written (winter session)
- Subject examined: Statistical analysis of network data
- Courses: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Fall
- Exam form: Written (winter session)
- Subject examined: Statistical analysis of network data
- Courses: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Fall
- Exam form: Written (winter session)
- Subject examined: Statistical analysis of network data
- Courses: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Fall
- Exam form: Written (winter session)
- Subject examined: Statistical analysis of network data
- Courses: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Fall
- Exam form: Written (winter session)
- Subject examined: Statistical analysis of network data
- Courses: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Fall
- Exam form: Written (winter session)
- Subject examined: Statistical analysis of network data
- Courses: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Fall
- Exam form: Written (winter session)
- Subject examined: Statistical analysis of network data
- Courses: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional