Courses
We teach several courses and seminars about social networks. These are part of the "Science in Perspective" program at ETH and can be attended by students from any study program. We further offer a specialized module on Social Networks for the MSc Data Science at ETH Zurich. From time to time, we teach block modules, for example, on dynamic social network analysis and data visualization that are open to interested researchers.
We study applications of network science methods.
Topics are selected for diversity in sports, research questions, and techniques with applications such as passing networks, team rankings, or
career trajectories.
Student teams present results from the recent literature, possibly with replication, in a conference format.
Network science as a paradigm is entering domains from engineering to the humanities but application is tricky.
By examples from recent research on sports analytics, 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.
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.
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.
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
* Influence
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.
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.
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.
Humans are connected by various social relations. When aggregated, we speak of social networks. This course discusses how social networks are structured, how they change over time and how they affect the individuals that they connect. It integrates social theory with practical knowledge of cutting-edge statistical methods and applications from a number of scientific disciplines.
The aim is to enable students to contribute to social networks research and to be discriminating consumers of modern literature on social networks. Students will acquire a thorough understanding of social networks theory (1), practical skills in cutting-edge statistical methods (2) and their applications in a number of scientific fields (3).
In particular, at the end of the course students will
- Know the fundamental theories in social networks research (1)
- Understand core concepts of social networks and their relevance in different contexts (1, 3)
- Be able to describe and visualize networks data in the R environment (2)
- Understand differences regarding analysis and collection of network data and other type of survey data (2)
- Know state-of-the-art inferential statistical methods and how they are used in R (2)
- Be familiar with the core empirical studies in social networks research (2, 3)
- Know how network methods can be employed in a variety of scientific disciplines (3)
Groups are ubiquitous in society and profoundly influence people's behaviour, attitudes, and opinions and how they interact and create social relationships. Social network researchers have always been interested in understanding what groups are, their formation, and their societal implications (e.g., for creative work, feeling integrated into a community, achieving common goals).
This seminar examines sociological, anthropological, management and social-psychological network research to identify how groups affect individuals and their social behaviour.
By the end of this seminar, students will be able to identify and compare different approaches to group theories through the lens of social network research. They will be familiar with the development and recent publications in the fields of social networks and social science and will be able to critically participate in several open debates in these fields. Among others, these debates are centred around the types and measurement of groups, challenges in understanding what groups are, the effects of groups on people’s feelings, thoughts, preferences, and behaviours (e.g., identification), and how social and cultural phenomena emerge (e.g., the diffusion of culture and the spread of social movements).
The following topics will be covered:
- What is a group?
- Social circles and groups in modern society.
- Emergence of cohesive subgroups and cognitive dissonance
- Clan, kinship, social roles, and communities
- Identification and self-categorisation with groups
- Teams, leaders, and their performance
- Social influence and the conformity to social norms
- Collecting data to analyse groups
These topics will be discussed considering the development of these topics through a social network perspective, recent research, and their measurement and analysis.
Soccer analytics refers to the use of data in tactical decision-making, recruitment, strategic planning, and fan engagement in association football. This course is first and foremost about data, problems, and methods. They are discussed, however, with reference to the broader context of measurement and data science in sports and society.
Students gain insight into the role of data science in professional football. They learn to capture aspects of the beautiful game in observable data to inform tactical, strategic, and communicative decision-making. By appreciating difficulties that arise even in activities with highly regulated interactions such as team sports, they reflect on the use of data science in the study of collective behavior.
This annual summer school covers basic and advanced topics in the
analysis, modeling, and visualization of networks in the context of
sociological theory and empirical research.