Multi pathways temporal distance unravels the hidden geometry of network-driven processes
Abstract Network-based interactions allow one to model many technological and natural systems, where understanding information flow between nodes is important to predict their functioning. The complex interplay between network connectivity and dynamics can be captured by scaling laws overcoming the...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-06-01
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| Series: | Communications Physics |
| Online Access: | https://doi.org/10.1038/s42005-023-01204-1 |
| _version_ | 1797806658092531712 |
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| author | Sebastiano Bontorin Manlio De Domenico |
| author_facet | Sebastiano Bontorin Manlio De Domenico |
| author_sort | Sebastiano Bontorin |
| collection | DOAJ |
| description | Abstract Network-based interactions allow one to model many technological and natural systems, where understanding information flow between nodes is important to predict their functioning. The complex interplay between network connectivity and dynamics can be captured by scaling laws overcoming the paradigm of information spread being solely dependent on network structure. Here, we capitalize on this paradigm to identify the relevant paths for perturbation propagation. We introduce a multi-pathways temporal distance between nodes that overcomes the limitation of focussing only on the shortest path. This metric predicts the latent geometry induced by the dynamics in which the signal propagation resembles the traveling wave solution of reaction-diffusion systems. We validate the framework on a set of synthetic dynamical models, showing that it outperforms existing approaches in predicting arrival times. On a set of empirical contact-based social systems, we show that it can be reliably used also for models of infectious diseases spread - such as the Susceptible-Infected-Susceptible - with remarkable accuracy in predicting the observed timing of infections. Our framework naturally encodes the concerted behavior of the ensemble of paths connecting two nodes in conveying perturbations, with applications ranging from regulatory dynamics within cells to epidemic spreading in social networks. |
| first_indexed | 2024-03-13T06:10:37Z |
| format | Article |
| id | doaj.art-95dbf7847ab347b9998338ce477cbf66 |
| institution | Directory Open Access Journal |
| issn | 2399-3650 |
| language | English |
| last_indexed | 2024-03-13T06:10:37Z |
| publishDate | 2023-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Physics |
| spelling | doaj.art-95dbf7847ab347b9998338ce477cbf662023-06-11T11:16:11ZengNature PortfolioCommunications Physics2399-36502023-06-01611810.1038/s42005-023-01204-1Multi pathways temporal distance unravels the hidden geometry of network-driven processesSebastiano Bontorin0Manlio De Domenico1Fondazione Bruno KesslerDepartment of Physics and Astronomy “Galileo Galilei”, University of PaduaAbstract Network-based interactions allow one to model many technological and natural systems, where understanding information flow between nodes is important to predict their functioning. The complex interplay between network connectivity and dynamics can be captured by scaling laws overcoming the paradigm of information spread being solely dependent on network structure. Here, we capitalize on this paradigm to identify the relevant paths for perturbation propagation. We introduce a multi-pathways temporal distance between nodes that overcomes the limitation of focussing only on the shortest path. This metric predicts the latent geometry induced by the dynamics in which the signal propagation resembles the traveling wave solution of reaction-diffusion systems. We validate the framework on a set of synthetic dynamical models, showing that it outperforms existing approaches in predicting arrival times. On a set of empirical contact-based social systems, we show that it can be reliably used also for models of infectious diseases spread - such as the Susceptible-Infected-Susceptible - with remarkable accuracy in predicting the observed timing of infections. Our framework naturally encodes the concerted behavior of the ensemble of paths connecting two nodes in conveying perturbations, with applications ranging from regulatory dynamics within cells to epidemic spreading in social networks.https://doi.org/10.1038/s42005-023-01204-1 |
| spellingShingle | Sebastiano Bontorin Manlio De Domenico Multi pathways temporal distance unravels the hidden geometry of network-driven processes Communications Physics |
| title | Multi pathways temporal distance unravels the hidden geometry of network-driven processes |
| title_full | Multi pathways temporal distance unravels the hidden geometry of network-driven processes |
| title_fullStr | Multi pathways temporal distance unravels the hidden geometry of network-driven processes |
| title_full_unstemmed | Multi pathways temporal distance unravels the hidden geometry of network-driven processes |
| title_short | Multi pathways temporal distance unravels the hidden geometry of network-driven processes |
| title_sort | multi pathways temporal distance unravels the hidden geometry of network driven processes |
| url | https://doi.org/10.1038/s42005-023-01204-1 |
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