Compressing strongly connected subgroups in social networks

An entropy-based approach

Datos Bibliográficos

ID10142311
AutoresDominic Brenner (FernUniversität in Hagen, Center of Logistics, Hagen, Germany), Andreas Dellnitz (0000-0002-9073-6403, FernUniversität in Hagen), Friedhelm Kulmann (FernUniversität in Hagen, autor de correspondencia), Wilhelm Rödder (FernUniversität in Hagen)
Año2017
Volumen41
Número2
Páginas84-103
Fecha de publicación2017-03-07
Peer ReviewedSí
Open AccessNo
TipoARTICLE
RevistaJournal of Mathematical Sociology (JOURNAL)
Identificadores de la revistaISSN: 0022-250X • E-ISSN: 1545-5874
EditorialTaylor & Francis (PUBLISHER • GB)
DOI10.1080/0022250x.2017.1284070
OpenAlexW2593253737
IdiomaEN
Referencias citadas29

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 (super-)actors’ information theoretical indices unchanged; i.e., such compression is information-invariant. The actual article relates on the reduction of networks with hundreds of actors. All entropy-based calculations are realized in an expert system shell

Embeddedness · Entropy (arrow of time · Physics · Social science · Sociology · Complex Network Analysis Techniques · Computer Science · Mathematics · Opinion Dynamics and Social Influence · Statistical Mechanics and Entropy · Theoretical Computer Science