Social Network Analysis
The aim of the course is to introduce students to social network analysis (SNA) and their instrumental value for businesses and the society. SNA encompasses techniques and methods for analyzing the constant flow of information over offline networks (e.g. networks of workers in labor markets, networks of organizations in product markets etc.) and online networks (e.g. Facebook posts, twitter feeds, google maps check-ins etc.) aiming to identify patterns of information propagation that are of interest to the analyst. The course will help students to understand the opportunities, challenges, and threats arising by the use of social networks as far as businesses and the society at large are concerned. The issues of innovation diffusion and information spread through networks will also be covered. Finally, students will be introduced to the concepts of the wisdom of the crowds and social learning, investigating the conditions under which opinion convergence (asymptotic learning) or herding may occur in social networks.
- Basic social network concepts (nodes, edges, network visualization);
- Network centrality, clustering and communities, strong and weak ties;
- Information diffusion, contagion and infection rates in social networks, thresholds and giant components, small-world phenomena;
- Aggregate behavior, opinion manipulation, convergence and consensus of beliefs, naïve and Bayesian learning and herding;
Practical applications of SNA will be addressed, although the course does not adopt an exclusively technical/mathematical perspective on subject coverage.