ABSTRACT | Social networks are complex and dynamic systems. Individual nodes in networks, however, do not necessarily overlook the network as a whole, but are mostly affected by their smaller (micro-level) neighborhoods. At the same time, emerging large-scale (macro-level) network outcomes such as segregation, cluster formation, or the distribution of knowledge have a direct impact on them and can restrict their opportunities to act. In the study of social network dynamics it is thus important to simultaneously consider two levels: the macro-level of large-scale network structures and the micro-level of individuals’ preferences, opportunities and actions. This talk illustrates how state-of-the-art statistical network methods and computational techniques can be combined to investigate the micro-macro link in social networks. Recent empirical work in the context of the Swiss StudentLife study will illustrate the value of this approach.
BIO | Christoph Stadtfeld is an Assistant Professor of Social Networks at ETH Zürich. His research focuses on the development and application of theories and statistical methods for social network dynamics. He holds a PhD from Karlsruhe Institute of Technology and has been postdoctoral researcher and Marie-Curie fellow at the University of Groningen, the Social Network Analysis Research Center in Lugano, and the MIT Media Lab. His work is published in leading sociological and interdisciplinary journals including Social Networks, Social Forces, Sociological Science, Sociological Methodology, and PNAS.