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Utrecht University Summer School on Network Science (2023): Javier Garcia-Bernardo, Leto Peel, Mahdi Shafiee Kamalabad, Elena Candellone, Vincent Buskens.
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Network Science
2023 Utrecht University Summer School
Practical information
- Dates: July 10th — July 14nd, 2023
- Location: Sjoerd Groenmangebouw room C0.28
- Instructors: Javier Garcia-Bernardo, Leto Peel, Mahdi Shafiee Kamalabad, Elena Candellone, 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. Statistical Approaches to Network Analysis: REM, ERGM, SOAM | Mahdi | Slides, Practical (zip) (Solution) | |
| 2b. Link prediction: Traditional approaches and node embeddings | Javier (& Leto) | Slides (part 1, part 2) Practical | |
| 3a. Network Reconstruction using Probabilistic Graphical Models: Markov Random Fields (Graphical LASSO) | Mahdi | Slides, Practical (zip) (Python example) | |
| 3b. Network Reconstruction using Probabilistic Graphical Models: Bayesian Networks | Mahdi | Slides, Practical (zip) | |
| 4a. Network Models and Hypothesis Testing | Leto | Slides, Calculation Example, Practical (Solution) | |
| 4b. Community structure: The Stochastic Block Model | Leto | Slides, Practical (Solution) | |
| 5. Simple and Complex Contagion in Networks | Vincent | Slides (morning, afternoon), Practical+data(ZIP) (Solution) |
The materials of previous editions are here: 2022
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.