Social networks are structures of relations between social actors - e.g., friendship between individuals, collaboration between companies, trade between countries, etc. They are important both in themselves and for explaining a variety of actor-dependent characteristics (behavioural tendencies, attitudes, performance, etc.) Particularly interesting is the two-way influence between relational networks and individual actions and outcomes. The data structure of social networks, where the usual statistical assumption of independent cases is totally implausible, poses special requirements for data analysis.
This course presents a number of important data analysis methods for social networks.
The material presented at this website is too extensive for the course. Only a selection of this will be treated.
The course is about five main topics:
1. Centrality and other positional characteristics of actors.
2. Concepts of positional equivalence.
3. The exponential random graph model (ERGM), a statistical model for single observations of networks.
4. Stochastic actor-based models for network dynamics (i.e., for analysing longitudinal network data).
5. Stochastic actor-based models for the simultaneous dynamics of networks and behaviour (which here is a term referring generally to changeable actor characteristics such as behavioural tendencies or performance).
SIENA is a program for the statistical analysis of network data, with the focus on social networks.
SIENA is designed for analyzing various types of data as dependent variables:
Longitudinal network data,
Longitudinal data of networks and behavior,
Cross-sectional network data.
A range of tools for social network analysis, including node and graph-level indices, structural distance and covariance methods, structural equivalence detection, p* modeling, random graph generation, and 2D/3D network visualization.