Robin Delabays, Giulia De Pasquale, Florian Dörfler, Yuanzhao Zhang
{"title":"从动力学重建超图","authors":"Robin Delabays, Giulia De Pasquale, Florian Dörfler, Yuanzhao Zhang","doi":"arxiv-2402.00078","DOIUrl":null,"url":null,"abstract":"A plethora of methods have been developed in the past two decades to infer\nthe underlying network structure of an interconnected system from its\ncollective dynamics. However, methods capable of inferring nonpairwise\ninteractions are only starting to appear. Here, we develop an inference\nalgorithm based on sparse identification of nonlinear dynamics (SINDy) to\nreconstruct hypergraphs and simplicial complexes from time-series data. Our\nmodel-free method does not require information about node dynamics or coupling\nfunctions, making it applicable to complex systems that do not have reliable\nmathematical descriptions. We first benchmark the new method on synthetic data\ngenerated from Kuramoto and Lorenz dynamics. We then use it to infer the\neffective connectivity among seven brain regions from resting-state EEG data,\nwhich reveals significant contributions from non-pairwise interactions in\nshaping the macroscopic brain dynamics.","PeriodicalId":501305,"journal":{"name":"arXiv - PHYS - Adaptation and Self-Organizing Systems","volume":"9 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Hypergraph reconstruction from dynamics\",\"authors\":\"Robin Delabays, Giulia De Pasquale, Florian Dörfler, Yuanzhao Zhang\",\"doi\":\"arxiv-2402.00078\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A plethora of methods have been developed in the past two decades to infer\\nthe underlying network structure of an interconnected system from its\\ncollective dynamics. However, methods capable of inferring nonpairwise\\ninteractions are only starting to appear. Here, we develop an inference\\nalgorithm based on sparse identification of nonlinear dynamics (SINDy) to\\nreconstruct hypergraphs and simplicial complexes from time-series data. Our\\nmodel-free method does not require information about node dynamics or coupling\\nfunctions, making it applicable to complex systems that do not have reliable\\nmathematical descriptions. We first benchmark the new method on synthetic data\\ngenerated from Kuramoto and Lorenz dynamics. We then use it to infer the\\neffective connectivity among seven brain regions from resting-state EEG data,\\nwhich reveals significant contributions from non-pairwise interactions in\\nshaping the macroscopic brain dynamics.\",\"PeriodicalId\":501305,\"journal\":{\"name\":\"arXiv - PHYS - Adaptation and Self-Organizing Systems\",\"volume\":\"9 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-01-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - PHYS - Adaptation and Self-Organizing Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2402.00078\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - PHYS - Adaptation and Self-Organizing Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2402.00078","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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 reliable
mathematical descriptions. We first benchmark the new method on synthetic data
generated from Kuramoto and Lorenz dynamics. We then use it to infer the
effective connectivity among seven brain regions from resting-state EEG data,
which reveals significant contributions from non-pairwise interactions in
shaping the macroscopic brain dynamics.