Skip to main content
Social Networks 1
Course Description

The aim of this course is to give an overview of the key ideas of network science from a social science perspective. The concept of networks has come to pervade modern society, as we routinely make use of online social networking services, as business gets organized into network forms, and as infectious diseases spread among contact networks. Network science is an interdisciplinary field, which aims at explaining such complex phenomena, emerging from simple principles of making links. This course gives an overview of some key concepts and research findings in social networks: weak ties and small worlds, power and centrality, homophily and segregation, structural holes and brokerage. The course also introduces key methods to record, analyze, and visualize network data. Students will be given access to diverse datasets for class purposes.

Learning Outcomes

Students taking this course should be able to understand basic concepts and methods from network science. Based on the knowledge they gained on the course, they will be able to critically interpret researches on social network and network science in general.

Assessment

Evaluation in the course is primarily based on a final project consisting of a written report and an oral presentation, where the students explore some of the concepts and methods introduced in class by performing a data-driven analysis of a dataset. Beyond the final project, students should also prepare an in-class presentation about a research paper (to be chosen from a list provided by the instructor) and participate in class discussions. Both the in-class presentation and the final project can be fulfilled in small groups – making it clear which part of the presentation/project belongs to which student.

Basis of Evaluation

Final project: 60%; Presentation: 30%; Class participation: 10%.

Prerequisites

Basic knowledge of statistics and basic skills in R or Python. Introductory material to R and Python will be provided for students who want to familiarize themselves with these programming languages in order to take the course.

Course Level
Master’s
Doctoral
Academic Year
2023-2024
Term
Fall
US Credits
2
ECTS Credits
4
Course Code
DNDS 6012