From 2 November 2020, the autumn semester 2020 will take place online. Exceptions: Courses that can only be carried out with on-site presence.
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851-0252-15L  Network Analysis

SemesterAutumn Semester 2018
LecturersU. Brandes
Periodicityyearly recurring course
Language of instructionEnglish
CommentParticularly suitable for students of D-INFK, D-MATH

AbstractNetwork science is a distinct domain of data science that is characterized by a specific kind of data being studied.
While areas of application range from archaeology to zoology, we concern ourselves with social networks for the most part.
Emphasis is placed on descriptive and analytic approaches rather than theorizing, modeling, or data collection.
ObjectiveStudents will be able to identify and categorize research problems
that call for network approaches while appreciating differences across application domains and contexts.
They will master a suite of mathematical and computational tools,
and know how to design or adapt suitable methods for analysis.
In particular, they will be able to evaluate such methods in terms of appropriateness and efficiency.
ContentThe following topics will be covered with an emphasis on structural and computational approaches and frequent reference to their suitability with respect to substantive theory:

* Empirical Research and Network Data
* Macro and Micro Structure
* Centrality
* Roles
* Cohesion
Lecture notesLecture notes are distributed via the associated course moodle.
Literature* Hennig, Brandes, Pfeffer & Mergel (2012). Studying Social Networks. Campus-Verlag.
* Borgatti, Everett & Johnson (2013). Analyzing Social Networks. Sage.
* Robins (2015). Doing Social Network Research. Sage.
* Brandes & Erlebach (2005). Network Analysis. Springer LNCS 3418.
* Wasserman & Faust (1994). Social Network Analysis. Cambridge University Press.
* Kadushin (2012). Understanding Social Networks. Oxford University Press.