851-0252-15L  Network Analysis

SemesterAutumn Semester 2020
LecturersU. Brandes
Periodicityyearly recurring course
Language of instructionEnglish
CommentParticularly suitable for students of D-INFK, D-MATH



Courses

NumberTitleHoursLecturers
851-0252-15 VNetwork Analysis2 hrs
Wed18:00-20:00ON LI NE »
U. Brandes

Catalogue data

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.

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits3 credits
ExaminersU. Brandes
Typegraded semester performance
Language of examinationEnglish
RepetitionRepetition possible without re-enrolling for the course unit.
Admission requirementBasic knowledge of discrete mathematics

Learning materials

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Only public learning materials are listed.

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Offered in

ProgrammeSectionType
Data Science MasterInterdisciplinary ElectivesWInformation
Doctoral Department of Humanities, Social and Political SciencesDoctoral and Post-Doctoral CoursesWInformation
GESS Science in PerspectiveSociologyWInformation
GESS Science in PerspectiveD-INFKWInformation
GESS Science in PerspectiveD-MATHWInformation
Integrated Building Systems MasterGESS Science in PerspectiveWInformation