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Abstract
Social Network Science is a distinct domain of data science that focuses on relational systems. Various models have been proposed to describe structures and dynamics of networks, including statistical and mathematical methods. In this course, the emphasis is placed on the statistical analysis of (social) systems and their connection to social theories and data sources.
Objective
The following topics will be covered: - Introduction to network models and their applications - Stylized models: * uniform random graph models * small world models * preferential attachment models - Models for testing hypotheses while controlling for the network structure: *Quadratic assignment procedure regression (QAP regression) - Models for testing hypotheses on the network structure: * Models for one single observation of a network: exponential random graph models (ERGMs) * Models for panel network data: stochastic actor-oriented models (SAOMs) The application of these models is illustrated through examples and practical sessions involving the analysis of network data using the software R.
Content
The following topics will be covered: - Introduction to network models and their applications - Stylized models: * uniform random graph models * small world models * preferential attachment models - Models for testing hypotheses while controlling for the network structure: *Quadratic assignment procedure regression (QAP regression) - Models for testing hypotheses on the network structure: * Models for one single observation of a network: exponential random graph models (ERGMs) * Models for panel network data: stochastic actor-oriented models (SAOMs) * Models for relational event data: dynamic network actor models (DyNAMs) The application of these models is illustrated through examples and practical sessions involving the analysis of network data using the software R.
Resources
Lecture Notes
Slides and lecture notes are distributed via the associated course moodle.
Literature
- Krackardt, D. (1987). QAP partialling as a test of spuriousness. Social networks, 9(2), 171-186. - Robins, G., Pattison, P., Kalish, Y., & Lusher, D. (2007). An introduction to exponential random graph (p*) models for social networks. Social networks, 29(2), 173-191. - Snijders, T. A. B., Van de Bunt, G. G., & Steglich, C. E. G. (2010). Introduction to stochastic actor-based models for network dynamics. Social networks, 32(1), 44-60. - Snijders, T. A. B. (2011). Statistical models for social networks. Annual Review of Sociology, 37. - Stadtfeld, C., & Block, P. (2017). Interactions, actors, and time: Dynamic network actor models for relational events. Sociological Science, 4, 318-352.