Social networks are formal representations of how individuals or other social actors interact or otherwise depend upon one another—who is friends with whom, which firms collaborate, who attends college courses together, etc.—Network approaches offer a good framework for understanding how a system of interdependent actors functions. While empirical social network analysis is most common in the study of small to medium sized social systems, at IAS we specialise in the analysis of large social systems. In particular, we empirically study the micro-to-macro link and various emergent phenomena, i.e., how well-understood local action patterns aggregate through complex dependencies to less-understood global outcomes.
Located at the intersection of empirical data analysis and the micro-modelling of macro phenomena, our research also seeks to develop novel statistical approaches. Due to its focus on interdependencies, network research is at odds with the far-reaching independence assumptions that underlie more common data analysis methods. Social network data require special, dedicated analysis techniques. Combined with the need to handle very large data sets, our work therefore regularly involves the development of new methods and models.
Our research focuses on topics such as labour mobility and residential mobility using data from Swedish national population registers to understand patterns of inequality and segregation. In other research, we investigate the consequences of online-facilitated connectedness—for example, the formation of opinion “echo chambers”—using network data derived from online databases.