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Network Modeling
Last Updated: 2026-02-05 16:02:00
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
- DS , DR , 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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Science in Perspective (In “Science in Perspective”-courses students learn to reflect on ETH’s STEM subjects from the perspective of humanities, political and social sciences. Only the courses listed below will be recognized as "Science in Perspective" courses.)
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Type A: Enhancement of Reflection Competence (SiP courses are recommended for bachelor students after their first-year examination and for all master- or doctoral students. All SiP courses are listed in Type A. Courses listed under Type B are only recommendations for enrollment for specific departments.)
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Type B: Reflection About Subject-Specific Methods and Contents (Subject-specific courses. Particularly relevant for students interested in those subjects. All these courses are also listed under the category “Typ A”, and every student can enroll in these courses.)
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Doctorate Humanities, Social and Political Sciences (More Information at: )
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