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Network Modeling
Last Updated: 2026-02-05 15:48:17
Abstract
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. Statistical and numerical methods have been developed to fit these models to empirical data. Emphasis is placed on the statistical analysis of (social) systems and their connection to social theories and data sources.
Objective
Students will be able to develop hypotheses that relate to the structures and dynamics of (social) networks, and tests those by applying advanced statistical network methods such as exponential random graph models (ERGMs) and stochastic actor-oriented models (SAOMs). Students will be able to explain and compare various network models, and develop an understanding of how those can be fit to empirical data. This will enable students to independently address research questions from various social science fields.
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.
General Information
- Language
- English
- Levels
- DR , DS , MSC
- Frequency
- Yearly recurring
Examination
- Type
- graded semester performance
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture | Network Modeling |
|
2 h weekly |
Offered In
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GESS Science in Perspective (Only the topics listed in this paragraph can be chosen as GESS Science in Perspective. Further below you will find the "type B courses Reflections about subject specific methods and content" as well as the language courses. 6 ECTS need to be acquired during the BA and 2 ECTS during the MA Students who already took a course within their main study program are NOT allowed to take the course again.)
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Type A: Enhancement of Reflection Competence (Suitable for all students. Students who already took a course within their main study program are NOT allowed to take the course again.)
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Type B: Reflection About Subject-Specific Methods and Contents (Subject-specific courses: Recommended for doctoral, master and bachelor students (after first-year examination only). Students who already took a course within their main study program are NOT allowed to take the course again. These course units are also listed under "Type A", which basically means all students can enroll)
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Doctoral Department of Humanities, Social and Political Sciences (More Information at: )
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