Clone the repository or check the course website: https://net-science.github.io/
Run code without downloading it (for now only available for the tutorials in Python):
- On binder:
- On Google Colab:
(you will need to install the libraries manually.
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.
- Javier Garcia-Bernardo
- Leto Peel
- Mahdi Shafiee Kamalabad
- Elena Candellone
- Vincent Buskens
This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License.