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Utrecht University Summer School on Network Science (2025): Javier Garcia-Bernardo, Leto Peel, Mahdi Shafiee Kamalabad, Elena Candellone, Jiamin Ou.
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Network Science
2026 Utrecht University Summer School
Practical information
- Dates: July 13th — July 17th, 2026
- Location: Victor J. Koningsberger building, room 2.28. Utrecht University, Science Park
- Registration: Registration for 2026 open: here
- Instructors: Javier Garcia-Bernardo, Leto Peel, Mahdi Shafiee Kamalabad, Elena Candellone, Jiamin Ou, Vincent Buskens
- Preparation: Install R, RStudio and Anacondas
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
| Day | Topic | Lecturer | Materials |
|---|---|---|---|
| Monday AM | Introduction to Network Science | Javier | |
| Monday PM | Network Representation and Centrality | Javier | |
| Tuesday AM | Network Models and Hypothesis Testing | Leto | |
| Tuesday PM | Statistical Approaches to Network Analysis: REM, ERGM, SOAM | Mahdi | |
| Wednesday AM | Community structure: The Stochastic Block Model | Leto | |
| Wednesday PM | Link prediction: Traditional approaches and node embeddings | Javier & Leto | |
| Thursday AM | Network Reconstruction using Probabilistic Graphical Models: Markov Random Fields (Graphical LASSO) | Mahdi | |
| Thursday PM | Network Reconstruction using Probabilistic Graphical Models: Bayesian Networks | Mahdi | |
| Friday | Simple and Complex Contagion in Networks | Jiamin |