Structural Cohesion: Visualization and Heuristics for Fast Computation

TL;DR

This paper enhances the measurement of structural cohesion in social networks by introducing heuristics that significantly speed up computation and a new visualization method, enabling analysis of large networks.

Contribution

It extends the classic structural cohesion model with average node connectivity, providing faster heuristics and a novel visualization for large network analysis.

Findings

Heuristics speed up computation by tenfold.

Applied methods to large collaboration networks.

Introduced a new graphical representation for cohesion analysis.

Abstract

The structural cohesion model is a powerful theoretical conception of cohesion in social groups, but its diffusion in empirical literature has been hampered by operationalization and computational problems. In this paper we start from the classic definition of structural cohesion as the minimum number of actors who need to be removed in a network in order to disconnect it, and extend it by using average node connectivity as a finer grained measure of cohesion. We present useful heuristics for computing structural cohesion that allow a speed-up of one order of magnitude over the algorithms currently available. We analyze three large collaboration networks (co-maintenance of Debian packages, co-authorship in Nuclear Theory and High-Energy Theory) and show how our approach can help researchers measure structural cohesion in relatively large networks. We also introduce a novel graphical representation of the structural cohesion analysis to quickly spot differences across networks.