Network science is a novel transdisciplinary approach to the study of complex systems. It integrates theoretical results with empirical “big data” analysis, discovering unexpected phenomena in real world systems. Relevant applications for economics and social sciences include: contagion in financial networks, information diffusion and aggregation, social percolation and opinion dynamics, transportation systems, epidemic spreading.

Network science relies on a tapestry of approaches and techniques drawn from very different fields. This course will provide a unified introduction, customized for undergraduate students in economics and accessible to students in social sciences.

This module offers an introduction to network science and its major applications within economics and aims to:


	provide an introduction to the fundamentals of graph theory;
	
	
	develop an understanding of the main network effects;
	
	
	impart an ability to recognise and analyse how networks affect market behaviour;
	
	
	illustrate some widely used concepts for the evaluation of networks.
	
On successful completion of this module, you should be able to:


	master the basics of graph theory;
	
	
	understand and interpret economic phenomena driven by network effects;
	
	
	create and perfect simple models of economic interactions in networks;
	
	
	design simple rules to generate or evaluate network models;
	
	
	access the economic literature based on network science.
	
You are provided with a study guide that details readings for each lecture, including a selection of theoretical and empirical articles. The main technical readings refer to the lecture notes and to selected chapters from the two accompanying textbooks. Additional readings are from articles accessible to undergraduate students, and are meant to either (a) provide an opportunity for research-based learning; or (b) offer a broader understanding of the practical relevance of the issues and of the current state of the art. Familiarity of the students with the readings prior to covering them in class makes it easier to cover all the material within the allocated lecture time and helps students develops the skills necessary for critically evaluating their sources of information.

There will also be five homework assignments, based on problem sets distributed throughout the course. Students are required to hand in two homework assignments of their choice, for which formative feedback will be provided. The homework assignments consist of questions with short verbal or numeric answers, aimed to prepare students for the midterm and final exams, as well as occasional (no more than three) open-end suggestions, aimed to stimulate research-related ideas. Solutions will be promptly provided and discussed in tutorials and office hours.

The combination of recommended readings, practical work on the problem sets, and open-end suggestions is very much aligned with UCL’s research-based learning philosophy.