Social Networks

About

Network Analysis is a rapidly growing field among quantitative social sciences. Networks permeate everyday life and contemporary social phenomena are increasingly represented as networks: from the participation to online social media, to the structure of relations in a working environment, to the description of political movements, to the analysis of the diffusion of banking crises. The boundaries of Network Science go well beyond social sciences, including applications to history, biology, glottology, statistics and computer science.

Network Analysis focuses on the relationship that exist between units of analysis, and studies those relationships through the tools of Graph Theory. This course will introduce the participants to Applied Network Analysis concentrating on measurement, data collection and manipulation, description and visualization of networks. Some inferential statistical techniques will be presented at the end of the course. Examples will be drawn from economics, political science, and sociology, and replication exercises will be discussed in class and implemented by the participants.

The course has a hands-on character, and the class will apply the concepts presented by the instructor using R packages and RStudio: R will be used most of the time, with some incursion into Gephi, for large networks visualisations. Knowledge of R is therefore recommended to get the best out of this short applied introduction to Social Network Analysis.

Course leader

Luca De Benedictis, PhD - University of Macerata

Target group

Professionals, Researchers, and Students, who wish to develop high-quality surveys and employ up-to-date statistical methods for survey data analysis. Potential attendees include survey practitioners, marketing professionals, social science students, and researchers.