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Utrecht University Summer School on Network Science (2026): Javier Garcia-Bernardo, Leto Peel, Mahdi Shafiee Kamalabad, Jiamin Ou.
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Network Science
2026 Utrecht University Summer School
Fifth edition
Practical information
- Dates: July 13th — July 17th, 2026
- Location: Victor J. Koningsberger building, room 2.28. Utrecht University, Science Park
- Daily start: Teaching starts at 9:00 AM each day.
- Registration: Registration for 2026 open: here
- Instructors: Javier Garcia-Bernardo, Leto Peel, Mahdi Shafiee Kamalabad, Jiamin Ou
- Preparation: Follow the installation instructions for R, RStudio, and Pixi.
Description
How can networks help us understand and predict social systems? How to find important individuals and communities? How to predict unobserved connections between genes? How to model the interdependencies between variables as a network? How to use machine learning techniques on networks? How can we stop disease spreading in networks? In this course, we provide participants with the conceptual and practical skills necessary to use network science tools to answer social, economic and biological questions.
This course introduces concepts and tools in network science. The objective of the course is that participants acquire hands-on knowledge on how to analyze different types of networks. Participants will be able to understand when a network approach is useful, understand different types of networks, understand the differences and similarities between a Network Science and a Social Network Analysis approach, describe network characteristics, infer edges or node attributes, and explore dynamical processes in networks.
The course has a hands-on focus, with lectures accompanied by programming practicals (in Python and R) to apply the knowledge on real networks, drawn from examples in sociology, economics and biology. You will also have time to work on your own data.
Bring your own data
We encourage participants to bring their own data and work with it during the practical sessions. This is a great opportunity to get feedback and guidance from the instructors. To make this feasible, the data should be structured as explained here.
Github repository
All slides, code and data can be found here. The lectures and code can also be explored using the links below.
Program
Each teaching day starts at 9:00 AM. Morning sessions run until lunch at 12:00; lunch is 12:00-13:00; afternoon sessions run 13:00-16:30.
| Day/time | Topic | Materials |
|---|---|---|
| Monday AM 09:00-12:00 |
Introduction to Network Science Lecturer: Javier |
|
| Monday PM 13:00-16:30 |
Network Representation and Centrality Lecturer: Javier |
|
| Tuesday AM 09:00-12:00 |
Network Models and Hypothesis Testing Lecturer: Leto |
|
| Tuesday PM 13:00-16:30 |
Community structure: The Stochastic Block Model Lecturer: Leto |
|
| Wednesday AM 09:00-12:00 |
Link prediction: Traditional approaches and node embeddings Lecturer: Javier & Leto |
|
| Wednesday PM 13:00-16:30 |
Statistical Approaches to Network Analysis: REM, ERGM, SOAM Lecturer: Mahdi |
|
| Thursday AM 09:00-12:00 |
Network Reconstruction using Probabilistic Graphical Models: Markov Random Fields (Graphical LASSO) Lecturer: Mahdi |
|
| Thursday PM 13:00-16:30 |
Network Reconstruction using Probabilistic Graphical Models: Bayesian Networks Lecturer: Mahdi |
|
| Friday 09:00-16:30 |
Simple and Complex Contagion in Networks Lecturer: Jiamin |