Ulrik Brandes: Catalogue data in Autumn Semester 2021 |
| Name | Prof. Dr. Ulrik Brandes |
| Name variants | Ulrik Brandes |
| Field | Social Networks |
| Address | Professur für Soziale Netzwerke ETH Zürich, WEP J 14 Weinbergstr.109 8006 Zürich SWITZERLAND |
| Telephone | +41 44 632 21 96 |
| ubrandes@ethz.ch | |
| Department | Humanities, Social and Political Sciences |
| Relationship | Full Professor |
| Number | Title | ECTS | Hours | Lecturers | |
|---|---|---|---|---|---|
| 851-0252-04L | Behavioral Studies Colloquium  | 0 credits | 2K | E. Stern, U. Brandes, D. Helbing, C. Hölscher, M. Kapur, C. Stadtfeld | |
| Abstract | This colloquium offers an opportunity to discuss recent and ongoing research and scientific ideas in the behavioral sciences, both at the micro- and macro-levels of cognitive, behavioral and social science. The colloquium features invited presentations from internal and external researchers as well as presentations of doctoral students close to submitting their dissertation research plan. | ||||
| Learning objective | Participants are informed about recent and ongoing research in different branches of the behavioral sciences. Presenting doctoral students obtain feedback on their dissertation research plan. | ||||
| Content | This colloquium offers an opportunity to discuss 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 covers a broad range of areas, including theoretical as well as empirical research in social psychology, 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. The colloquium features invited presentations from internal and external researchers as well as presentations of doctoral students close to submitting their dissertation research plan. | ||||
| Prerequisites / Notice | Doctoral students in D-GESS can obtain 2 credit points for presenting their dissertation research plan. | ||||
| 851-0252-15L | Network Analysis Particularly suitable for students of D-INFK, D-MATH | 3 credits | 2V | U. Brandes | |
| 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. | ||||
| Learning 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 * Centrality * Roles * Cohesion | ||||
| 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. | ||||
| 851-0586-03L | Applied Network Science: Social Media Networks  Number of participant limited to 20 | 3 credits | 1S | U. Brandes | |
| Abstract | We study applications of network science methods, this semester in the domain of social media. Topics are selected for diversity in research questions and techniques for topics such as privacy and information spread on a variety of platforms. Student teams present results from the recent literature, possibly with replication, in a one-day conference. | ||||
| Learning objective | Network science as a paradigm is entering domains from engineering to the humantities but application is tricky. By examples from recent research on social media, students learn to appreciate that, and how, context matters. They will be able to assess the appropriateness of approaches for substantive research problems, and especially when and why quantitative approaches are or are not suitable. | ||||