Description

What can network analysis help us see that standard models often miss?

For many social, economic, biological, and financial systems, the structure of the data matters: who is connected to whom, how clusters form, which nodes act as bridges, and how information, behavior, innovation, disease, or risk can move through a network.

From friendship networks and online communities to migration, organizations, political polarization, health, education, economic exchange, financial transactions, ownership structures, supply chains, and systemic risk, some of the most important patterns only become visible when we study connections.

Join our Summer School on Network Science
Utrecht | July 13–17, 2026

In one week, we will cover how to:

• represent complex systems as networks
• describe network structure and identify central actors, brokers, and communities
• model network formation and community detection
• work with statistical and machine learning approaches for network data
• study link prediction, network inference, and contagion processes
• apply the methods hands-on in Python and R using real data

The course is aimed at participants who want practical tools for working with relational data in their own research.

Bring your own data, questions, and use cases.

Register here:
https://utrechtsummerschool.nl/courses/data-science/data-science-network-science

Entry requirements

Participants should be proficient in spoken and written English. Participants should feel comfortable programming in either Python or R (we will be using both in the course), and have a basic understanding of algebra, probability and statistics. If participants only know either Python or R, following a short introduction course for the other language is strongly recommended.

Teaching methods/learning formats

Each day is split into a morning and an afternoon session. In each session we first introduce a method with a focus on conceptual understanding and possible applications. This is followed by a practical in which the participants apply the method learned using real data from socioeconomic or biological settings.
During the in-class practicals, participants will have the opportunity to discuss how to apply the methods to their own data.

Participants are requested to bring their own laptop computer. Software will be freely available online.

Programme

• Day 1: Introduction to network science and network description
• Day 2: Network formation models and statistical approaches to network analysis
• Day 3: Community detection and link prediction
• Day 4: Network Inference
• Day 5: Simple and Complex Contagion in Networks