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A framework based on generalized hierarchical random graphs (GHRGs) for thedetection of change points in the structure of temporal networks has recentlybeen developed by Peel and Clauset [1]. We build on this methodology and extendit to also include the versatile stochastic block models (SBMs) as a parametricfamily for reconstructing the empirical networks. We use five differenttechniques for change point detection on prototypical temporal networks,including empirical and synthetic ones. We find that none of the consideredmethods can consistently outperform the others when it comes to detecting andlocating the expected change points in empirical temporal networks. Withrespect to the precision and the recall of the results of the change points, wefind that the method based on a degree-corrected SBM has better recallproperties than other dedicated methods, especially for sparse networks andsmaller sliding time window widths.

Detection and localization of change points in temporal networks with the aid of stochastic block models