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Utrecht University Summer School on Network Science (2024): Javier Garcia-Bernardo, Leto Peel, Mahdi Shafiee Kamalabad, Elena Candellone, Vincent Buskens, Jiamin Ou.
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Network Science
2024 Utrecht University Summer School
Practical information
- Dates: July 15th — July 19nd, 2024
- Location: Victor J. Koningsberger building, room 2.28. Utrecht University, Science Park
- Registration: Closed for 2024, the registration for the 2025 edition will be here
- Instructors: Javier Garcia-Bernardo, Leto Peel, Mahdi Shafiee Kamalabad, Elena Candellone, Jiamin Ou, Vincent Buskens
- Preparation: Install R, RStudio and Anacondas
Github repository
All slides, code and data can be found here. The lectures and code can also be explored using the links below.
Program
| Topic | Lecturer | Materials |
|---|---|---|
| 1a. Introduction to Network Science | Javier |
Slides, Practical Python (Solution), Practical R |
| 1b. Network Representation and Centrality | Javier |
Slides, Practical (Solution) |
| 2a. Network Models and Hypothesis Testing | Leto |
Slides, Graph-tool example, Practical (Solution) |
| 2b. Statistical Approaches to Network Analysis: REM, ERGM, SOAM | Mahdi |
Slides, Practical (zip) Questions: part 1, part 2 Solutions: part 1, part 2 |
| 3a. Community structure: The Stochastic Block Model | Leto |
Slides, Practical (Solution) |
| 3b. Link prediction: Traditional approaches and node embeddings | Javier & Leto |
Slides (part 1, part 2) Practical |
| 4a. Network Reconstruction using Probabilistic Graphical Models: Markov Random Fields (Graphical LASSO) | Mahdi |
Slides, Practical (zip): Questions and Solutions (Python example) |
| 4b. Network Reconstruction using Probabilistic Graphical Models: Bayesian Networks | Mahdi |
Slides, Practical (zip): Questions and Solutions |
| 5. Simple and Complex Contagion in Networks | Vincent, Jiamin |
Slides (morning, afternoon), Practical (zip) |
The materials of previous editions are here: 2022, 2023
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 learn the dependencies between interrelated entities? 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 Complex Networks 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.