Hypergraph reconstruction from dynamics
Abstract A plethora of methods have been developed in the past two decades to infer the underlying network structure of an interconnected system from its collective dynamics. However, methods capable of inferring nonpairwise interactions are only starting to appear. Here, we develop an inference alg...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-03-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-57664-2 |
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| _version_ | 1850094808883789824 |
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| author | Robin Delabays Giulia De Pasquale Florian Dörfler Yuanzhao Zhang |
| author_facet | Robin Delabays Giulia De Pasquale Florian Dörfler Yuanzhao Zhang |
| author_sort | Robin Delabays |
| collection | DOAJ |
| description | Abstract A plethora of methods have been developed in the past two decades to infer the underlying network structure of an interconnected system from its collective dynamics. However, methods capable of inferring nonpairwise interactions are only starting to appear. Here, we develop an inference algorithm based on sparse identification of nonlinear dynamics (SINDy) to reconstruct hypergraphs and simplicial complexes from time-series data. Our model-free method does not require information about node dynamics or coupling functions, making it applicable to complex systems that do not have a reliable mathematical description. We first benchmark the new method on synthetic data generated from Kuramoto and Lorenz dynamics. We then use it to infer the effective connectivity in the brain from resting-state EEG data, which reveals significant contributions from non-pairwise interactions in shaping the macroscopic brain dynamics. |
| format | Article |
| id | doaj-art-00822d8a433d4bc8ad95ea232c554393 |
| institution | DOAJ |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-00822d8a433d4bc8ad95ea232c5543932025-08-20T02:41:34ZengNature PortfolioNature Communications2041-17232025-03-011611910.1038/s41467-025-57664-2Hypergraph reconstruction from dynamicsRobin Delabays0Giulia De Pasquale1Florian Dörfler2Yuanzhao Zhang3School of Engineering, University of Applied Sciences of Western Switzerland HES-SODepartment of Electrical Engineering, Eindhoven University of TechnologyDepartment of Information Technology and Electrical Engineering, ETH ZürichSanta Fe InstituteAbstract A plethora of methods have been developed in the past two decades to infer the underlying network structure of an interconnected system from its collective dynamics. However, methods capable of inferring nonpairwise interactions are only starting to appear. Here, we develop an inference algorithm based on sparse identification of nonlinear dynamics (SINDy) to reconstruct hypergraphs and simplicial complexes from time-series data. Our model-free method does not require information about node dynamics or coupling functions, making it applicable to complex systems that do not have a reliable mathematical description. We first benchmark the new method on synthetic data generated from Kuramoto and Lorenz dynamics. We then use it to infer the effective connectivity in the brain from resting-state EEG data, which reveals significant contributions from non-pairwise interactions in shaping the macroscopic brain dynamics.https://doi.org/10.1038/s41467-025-57664-2 |
| spellingShingle | Robin Delabays Giulia De Pasquale Florian Dörfler Yuanzhao Zhang Hypergraph reconstruction from dynamics Nature Communications |
| title | Hypergraph reconstruction from dynamics |
| title_full | Hypergraph reconstruction from dynamics |
| title_fullStr | Hypergraph reconstruction from dynamics |
| title_full_unstemmed | Hypergraph reconstruction from dynamics |
| title_short | Hypergraph reconstruction from dynamics |
| title_sort | hypergraph reconstruction from dynamics |
| url | https://doi.org/10.1038/s41467-025-57664-2 |
| work_keys_str_mv | AT robindelabays hypergraphreconstructionfromdynamics AT giuliadepasquale hypergraphreconstructionfromdynamics AT floriandorfler hypergraphreconstructionfromdynamics AT yuanzhaozhang hypergraphreconstructionfromdynamics |