Generating surrogate temporal networks from mesoscale building blocks
Abstract 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...
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Nature Portfolio
2025-04-01
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| Series: | Communications Physics |
| Online Access: | https://doi.org/10.1038/s42005-025-02075-4 |
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| author | Giulia Cencetti Alain Barrat |
| author_facet | Giulia Cencetti Alain Barrat |
| author_sort | Giulia Cencetti |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-c31bbe5eb74547ebaeede127fdbc00a7 |
| institution | OA Journals |
| issn | 2399-3650 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Physics |
| spelling | doaj-art-c31bbe5eb74547ebaeede127fdbc00a72025-08-20T02:17:54ZengNature PortfolioCommunications Physics2399-36502025-04-018111510.1038/s42005-025-02075-4Generating surrogate temporal networks from mesoscale building blocksGiulia Cencetti0Alain Barrat1Aix Marseille Univ, Université de Toulon, CNRS, CPTAix Marseille Univ, Université de Toulon, CNRS, CPTAbstract 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.https://doi.org/10.1038/s42005-025-02075-4 |
| spellingShingle | Giulia Cencetti Alain Barrat Generating surrogate temporal networks from mesoscale building blocks Communications Physics |
| title | Generating surrogate temporal networks from mesoscale building blocks |
| title_full | Generating surrogate temporal networks from mesoscale building blocks |
| title_fullStr | Generating surrogate temporal networks from mesoscale building blocks |
| title_full_unstemmed | Generating surrogate temporal networks from mesoscale building blocks |
| title_short | Generating surrogate temporal networks from mesoscale building blocks |
| title_sort | generating surrogate temporal networks from mesoscale building blocks |
| url | https://doi.org/10.1038/s42005-025-02075-4 |
| work_keys_str_mv | AT giuliacencetti generatingsurrogatetemporalnetworksfrommesoscalebuildingblocks AT alainbarrat generatingsurrogatetemporalnetworksfrommesoscalebuildingblocks |