Science of social and economic networks

Objective

Aims

The study of social networks accompanied the development of quantitative sociology, and it is now blossoming through the joint contribution of multiple disciplines, including physics and applied mathematics. It is also developing within economics, shedding light on non-market interactions that affect trade, labor markets and the diffusion of innovations.

The goal of the course is to provide students with fundamental insight into network science and how it can be used in sociology, economics, management, politics, and data science. We will present essential concepts and theories at the intersection of different disciplines, showing applications to empirical problems. There will be elements of research design, focusing on how network science can be successfully integrated into a social science research project, and we will give you confidence in using key analytical tools and techniques with real-world data.

All social science and data science backgrounds are welcome, and students are not assumed to have any previous knowledge of network science. Prior familiarity with Python is helpful but not essential.

Learning Outcomes

On attending this course, you will be able to:

• Demonstrate knowledge of the key principles, approaches and achievements of network science
• Understand network data type, source and format
• Compute and interpret network metrics to analyze network data
• Visualize network data
• Discuss similarities and differences between network science and classical social science
• Develop network-oriented research questions
• Design data analysis approaches for network data
• Apply basic modelling principles for network data

Learning and teaching activities

We will meet eight times between January-March for three-hour sessions. Each session will consist of two parts, a lecture to present and discuss the main theories, and a tutorial to provide you with hands-on experience in network analysis and data visualization.

It is recommended that students bring their laptop with them. The proposed exercises will be done in Python.

There is also an important component of self-study with structured materials provided in class.

Assessment

Two in-class tests (25% each) and final project (50%).

Every week except holidays and the last session, you will be assigned a take-home exercise to consolidate your understanding of the ideas presented in class, to apply them in practice or to expand your knowledge of them. Some of these exercises are to be done individually, others in smal