Compressing strongly connected subgroups in social networks -- an entropy-based approach
Brenner, D.
Dellnitz, A.
Kulmann, F.
Rödder, W.
Probabilistische Wissensverarbeitung
The Journal of Mathematical Sociology, 41 (2017) 84-103. DOI: 10.1080/0022250X.2017.1284070.

To detect and study cohesive subgroups of actors is a main objective in social network analysis. What are the respective relations inside such groups and what separates them from the outside. Entropy-based analysis of network structures is an up-and-coming approach. It turns out to be a powerful instrument to detect certain forms of cohesive subgroups and to compress them to superactors without loss of information about their embeddedness in the net: Compressing strongly connected subgroups leaves the whole net's and the \mbox{(super-)} actors' information theoretical indices unchanged; i.e. such compression is information-invariant. The actual paper relates on the reduction of networks with hundreds of actors. All entropy-based calculations are realized in an expert system shell.

Keywords: Information theory -- Entropy -- Social Networks -- Graph compression -- Network analysis