851-0252-15L Network Analysis
|Semester||Autumn Semester 2018|
|Periodicity||yearly recurring course|
|Language of instruction||English|
|Comment||Particularly suitable for students of D-INFK, D-MATH|
|Abstract||Network 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.
|Objective||Students 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.
|Content||The 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
|Lecture notes||Lecture 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.