Network Science

Course code
Course fee (excl. housing)
Course Level

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.


  • Day 1: Introduction to networks and network description
  • Day 2: Network formation models
  • Day 3: Network inference and machine learning approaches
  • Day 4: Networks to represent models
  • Day 5: Dynamics in networks

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 basic understanding of algebra, probability and statistics.

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.

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

Day-to-day programme (PDF)
Course director
Javier Garcia-Bernardo

Target audience

Participants with some technical background eager to learn about network science.


Course fee:
Fee covers
Course + course materials + lunch
Housing fee:
Housing cost
Housing provider:
Utrecht Summer School
Extra information about the fee

Tuition fee for PhD students from the Faculty of Social and Behavioural Sciences from Utrecht University will be funded by the Graduate School of Social and Behavioural Sciences.

Utrecht Summer School does not offer scholarships for this course.

More information

Irma Reyersen | E:


Application deadline: 
Registration deadline
04 July 2022