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Applications of Cohesive Subgraph Detection Algorithms to Analyzing Socio-Technical Networks

Socio-technical networks can be productively 
modeled at several granularities, including the 
interaction of actors, how this interaction is mediated 
by digital artifacts, and sociograms that model direct 
ties between the actors themselves. Cohesive subgraph 
detection algorithms (CSDA, a.k.a. “community 
detection algorithms”) are often applied to 
sociograms, but also have utility in analyzing graphs 
corresponding to other levels of modeling. This paper 
illustrates applications of CSDA to graphs modeling 
interaction and mediated association. It reviews some 
leading candidate algorithms (particularly InfoMap, 
link communities, the Louvain method, and weakly 
connected components, all of which are available in 
R), and evaluates them with respect to how useful they 
have been in analyzing a large dataset derived from a 
network of educators known as Tapped In. This 
practitioner-oriented evaluation is a complement to 
more formal benchmark based studies common in the 
literature.