Network Science

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 or information 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.

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.

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 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. A strong foundation for this course can be obtained through our winter courses Introduction to R and Introduction to Python or our summer course Data Science: Statistical Programming with R (Course code S24).

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.

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

We also offer tailor-made M&S courses and in-house M&S training. If you want to look at the possibilities, please contact Dr. Laurence Frank at pe.dsai@uu.nl.