Statistical analysis of network data

MATH-448 / 5 credits

Teacher: Olhede Sofia Charlotta

Language: English

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 ·

 

Ressources en bibliothèque

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

Reference week

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