Data Science: Network Science

Description

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. In case 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 school courses Introduction to R and Introduction to Python or our Summer school 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.

Target audience

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

For an overview of all our summer courses offered by the Department of Methodology and Statistics please click here.

Aim of the course

At the end of the summer school, the participants will be able to:
-  Understand fundamental concepts in network science and when a network approach is advantageous;
-  Describe the types of analyses available to analyze networks;
-  Calculate different network descriptive statistics;
-  Evaluate which centrality measures are more useful for finding the most important actors in a network;
-  Describe which statistical analysis can be applied to social network data;
-  Apply the relational event model to study the evolution of social interactions;
-  Use probabilistic graphical models for network reconstruction;
-  Evaluate different techniques of link prediction;
-  Construct network models to test hypotheses about network properties and characteristics;
-  Discover communities (clusters) in networks;
-  Explore dynamics of contagion in networks.

Study load

Five full days. A typical course day starts at 9:00 hours and ends at 17:00 hours, with breaks for coffee and tea, lunch, and sodas.

You will receive a certificate upon course completion. Please be aware that this course does not include graded activities, and therefore we cannot provide a transcript of grades.

Tags

• Network science
• network inference
• social network analysis
• Bayesian networks
• graphical models
• centrality
• community detection
• Data Science
• M&S