Generating surrogate temporal networks from mesoscale building blocks

Surrogate networks can constitute suitable replacements for real networks, in particular to study dynamical processes on networks, when only incomplete or limited datasets are available. As empirical datasets most often present complex features and interplays between structure and temporal evolution, creating surrogate data is however a challenging task, in particular for data describing time-resolved interactions between agents. Here we propose a method to generate surrogate temporal networks that mimic such observed datasets. The method is based on a decomposition of original datasets into temporal subnetworks encoding local structures on a short time scale. These are used as building blocks to generate new synthetic temporal networks that will hence inherit the shape of local interactions from the datasets. Moreover, we take into account larger scale correlations on structural and temporal dimension, using them to inform the process of assembling the building blocks. We showcase the method by generating surrogate networks for several datasets of social interactions and comparing them to the original data. First, we show that surrogate data possess complex structural and temporal features similar to the ones of the original data. Second, we simulate several dynamical processes and compare their outcome on the generated and original datasets. Surrogate networks can constitute suitable replacements for real networks when only incomplete or limited datasets are available, especially to study dynamical processes such as spreading of diseases or information. This study proposes a method to generate surrogate networks that mimic key statistical and structural features of the observed datasets.