In the context of big data and data driven research we often forget that Social Scientists have been spending the last hundred years to understand social structures and dynamics. In the area of social network analysis lots of metrics have been developed to assess the importance of nodes and groups or to reason about networks based on their structure. In addition, a large corpus of empirical data about networks is available and theories have been developed that describe network dynamics. This knowledge can also be applied to nonhuman network data. However, different networks require different interpretations of the same metrics and knowing your metrics and you network data is crucial for creating compelling analyses. Even in the context of algorithmic optimization, knowledge about the social/technical “reality” of the network can be very helpful.
Jürgen Pfeffer’s research focus lies in the computational analysis of organizations and societies with a special emphasis on large-scale systems. He is particularly interested in methodological and algorithmic questions as well as challenges arising from analyzing such systems. His research combines traditional network analysis and dynamic network analysis theories and methods with up-to-date science from the areas of visual analytics, geographic information systems, system dynamics, and data mining. Most of Pfeffer’s work is at the intersection of computer science and social science.
Faculty Host: José Moura
junez [atsymbol] andrew.cmu.edu