Iterated Community Detection in Psychological Networks

Psychological network models often feature communities: subsets of nodes that are more densely connected to themselves than to other nodes. The Spinglass algorithm is a popular method of detecting communities within a network, but it is a nondeterministic algorithm, meaning that the results can vary from one iteration to the next. There is no established method for determining the optimal solution or for evaluating instability across iterations in the emerging discipline of network psychometrics. We addressed this need by introducing and evaluating iterated community detection: Spinglass (IComDetSpin), a method for aggregating across multiple Spinglass iterations to identify the most frequent solution and quantify and visualize the instability of the solution across iterations. In two simulation studies, we evaluated (a) the performance of IComDetSpin in identifying the true community structure and (b) information about the fuzziness of community boundaries; information that is not available with a single iteration of Spinglass. In Study 1, IComDetSpin outperformed single-iteration Spinglass in identifying the true number of communities and performed comparably to Walktrap. In Study 2, we extended our evaluation to networks estimated from simulated data and found that both IComDetSpin and Exploratory Graph Analysis (a well-established community detection method in network psychometrics) performed well and that IComDetSpin outperformed Exploratory Graph Analysis when correlations between communities were high and number of nodes per community was lower (5 vs. 10). Overall, IComDetSpin improved the performance of Spinglass and provided unique information about the stability of community detection results and fuzziness in community structure.