22 July 2022
on course website
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
Day 2: Network structure and reference models
Day 3: Network inference, including Bayesian Networks
Day 4: Social Network Analysis
Day 5: Dynamical networks
Participants should be proficient in spoken and written English. Participants should feel comfortable programming in either Python or R, 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.
Participants with some technical background eager to learn about network science.
EUR 720: Course feeRegister for this course
on course website