Christoph Stadtfeld: Catalogue data in Autumn Semester 2020

Name Prof. Dr. Christoph Stadtfeld
FieldSocial Networks
Professur für Soziale Netzwerke
ETH Zürich, WEP J 16
8092 Zürich
Telephone+41 44 632 07 93
DepartmentHumanities, Social and Political Sciences
RelationshipAssociate Professor

851-0252-04LBehavioral Studies Colloquium Information 0 credits2KD. Helbing, U. Brandes, C. Hölscher, M. Kapur, C. Stadtfeld, E. Stern
AbstractThis colloquium is about recent and ongoing research and scientific ideas in the behavioral sciences, both at the micro- and macro-levels of cognitive, behavioral and social science. It features invited presentations from internal and external researchers as well as presentations of doctoral students close to submitting their dissertation research plan.
ObjectiveParticipants are informed about recent and ongoing research in the field. Presenting doctoral students obtain feedback on their dissertation research plan.
ContentThe covers the broadly understood field of behavioral science, including theoretical as well as empirical research in Social Psychology and Research on Higher Education, Sociology, Modeling and Simulation in Sociology, Decision Theory and Behavioral Game Theory, Economics, Research on Learning and Instruction, Cognitive Psychology and Cognitive Science.
Prerequisites / NoticeDoctoral students in D-GESS can obtain 2 credits for presenting their dissertation research plan.
851-0252-13LNetwork Modeling
Particularly suitable for students of D-INFK

Students are required to have basic knowledge in inferential statistics, such as regression models.
3 credits2VC. Stadtfeld, V. Amati
AbstractNetwork 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.
ObjectiveStudents 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.
ContentThe 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.
Lecture notesSlides 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.
Prerequisites / NoticeStudents are required to have basic knowledge in inferential statistics and should be familiar with linear and logistic regression models.