Universität KonstanzExzellenzcluster: Kulturelle Grundlagen von Integration

Social Network Algorithmics

Prof. Dr. Ulrik Brandes


This project is to explore, whether a genuinely algorithmic approach can serve to finally bridge what has been termed the theory-gap in social network analysis. While there is considerable doubt that it can even exist, a coherent methodological basis would constitute a major breakthrough for the rising network paradigm. In an unusual attempt, algorithmic research will used to systematically identify hidden theoretical assumptions and consequences of current methodology, and thus develop more deeply rooted foundations for empirical social network analysis.

The network perspective has proven incredibly convincing and fruitful in various areas of science, the social and life sciences in particular, and its adoption is rapid. Nevertheless, there is a surprisingly wide gap between the face value of social network analysis and its methodological stringency motivating this project. Social network analysis is attracting researchers and practitioners in many branches of the social sciences and humanities, but among theoretically and methodologically oriented empirical researchers alike there has always been a strong scepticism with regard to its validity. This is because there is no sustainable link between theoretical underpinnings and computational analyses.

The study of social networks is based on the reasoning that seemingly autonomous individuals and organizations are in fact embedded in social relations and interactions. The term was coined by Barnes to delineate this decidedly structuralist perspective from research traditions on social groups and social categories.

While the network perspective is spreading, it is not at the maturity level of other approaches, yet. A particular deficiency pointed out most prominently by Marsden is the weak linkage between network formation theories, data collection, and methods employed for analysis, i.e., the matching of formal network analysis with the way that networks are conceptualized and measured Without an increase in coherence, the upcoming challenges arising from size and dynamics may prove to be critical, no matter how many more technically sophisticated methods will be devised. On the other hand, the situation provides an ideal opportunity to rethink the very foundations of a field that is expected to have significant impact on a number of disciplines.
My goal is therefore to improve the foundations of the social network paradigm by studying theory-method dependencies with algorithmic means.