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从网络到复杂系统的最优高阶模型。

From networks to optimal higher-order models of complex systems.

作者信息

Lambiotte Renaud, Rosvall Martin, Scholtes Ingo

机构信息

University of Oxford, United Kingdom.

Integrated Science Lab, Department of Physics, Umeå University, Sweden.

出版信息

Nat Phys. 2019 Apr;15(4):313-320. doi: 10.1038/s41567-019-0459-y. Epub 2019 Mar 25.

DOI:10.1038/s41567-019-0459-y
PMID:30956684
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6445364/
Abstract

Rich data is revealing that complex dependencies between the nodes of a network may escape models based on pairwise interactions. Higher-order network models go beyond these limitations, offering new perspectives for understanding complex systems.

摘要

丰富的数据表明，网络节点之间的复杂依赖关系可能会使基于成对相互作用的模型失效。高阶网络模型突破了这些限制，为理解复杂系统提供了新的视角。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/80e1/6445364/bdb1023b5969/emss-81685-f006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/80e1/6445364/ac659f6cd9b8/emss-81685-f001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/80e1/6445364/f88485c01277/emss-81685-f002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/80e1/6445364/1ee639316a7b/emss-81685-f003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/80e1/6445364/4a7521c5f642/emss-81685-f004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/80e1/6445364/afb5ef6aa8fe/emss-81685-f005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/80e1/6445364/bdb1023b5969/emss-81685-f006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/80e1/6445364/ac659f6cd9b8/emss-81685-f001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/80e1/6445364/f88485c01277/emss-81685-f002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/80e1/6445364/1ee639316a7b/emss-81685-f003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/80e1/6445364/4a7521c5f642/emss-81685-f004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/80e1/6445364/afb5ef6aa8fe/emss-81685-f005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/80e1/6445364/bdb1023b5969/emss-81685-f006.jpg

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3
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4
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5
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Front Hum Neurosci. 2025 Apr 16;19:1543854. doi: 10.3389/fnhum.2025.1543854. eCollection 2025.
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4
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