All slides, code and data can be found here. The lectures and code can also be explored using the links below.
|1a. Introduction to Network Science||Javier||Slides, Practical Python (Solution), Practical R|
|1b. Network Representation and Centrality||Javier||Slides, Practical (Solution)|
|2a. Network Models and Hypothesis Testing||Leto||Slides, Practical (Solution)|
|2b. Statistical Approaches to Network Analysis: REM, ERGM, SOAM||Mahdi||Slides, Practical+data(ZIP) (Solutions (html))|
|3a. Community structure: The Stochastic Block Model||Leto||Slides, Practical (Solutions)|
|3b. Link prediction: Traditional approaches and node embeddings||Leto & Javier||Slides (part 1, part 2) Practical|
|4a. Network Reconstruction using Probabilistic Graphical Models: Markov Random Fields (Graphical LASSO)||Mahdi||Slides, Practical+data(ZIP) (Solutions (html)) (Python example)|
|4b. Network Reconstruction using Probabilistic Graphical Models: Bayesian Networks||Mahdi||Slides, Practical+data(ZIP) (Solutions (html))|
|5. Simple and Complex Contagion in Networks||Vincent & Jiamin||Slides (morning, afternoon), Practical+data(ZIP)|
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.