Higher-order random network models
Most existing random network models that describe complex systems in nature and society are developed through connections that indicate a binary relationship between two nodes. However, real-world networks are so complicated that we can only identify many critical hidden structural properties throug...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2024-01-01
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| Series: | New Journal of Physics |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1367-2630/ad106a |
| _version_ | 1797354143453544448 |
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| author | Jinyu Huang Youxin Hu Weifu Li Maoyan Lin |
| author_facet | Jinyu Huang Youxin Hu Weifu Li Maoyan Lin |
| author_sort | Jinyu Huang |
| collection | DOAJ |
| description | Most existing random network models that describe complex systems in nature and society are developed through connections that indicate a binary relationship between two nodes. However, real-world networks are so complicated that we can only identify many critical hidden structural properties through higher-order structures such as network motifs. Here we propose a framework in which we define higher-order stubs, higher-order degrees, and generating functions for developing higher-order complex network models. Then we develop higher-order random networks with arbitrary higher-order degree distributions. The developed higher-order random networks share critical structural properties with real-world networks, but traditional connection-based random networks fail to exhibit these structural properties. For example, as opposed to connection-based random network models, the proposed higher-order random network models can generate networks with power-law higher-order degree distributions, right-skewed degree distributions, and high average clustering coefficients simultaneously. These properties are also observed on the Internet, the Amazon product co-purchasing network, and collaboration networks. Thus, the proposed higher-order random networks are necessary supplements to traditional connection-based random networks. |
| first_indexed | 2024-03-08T13:45:17Z |
| format | Article |
| id | doaj.art-4029721104764fd8b135669e2cc6bc79 |
| institution | Directory Open Access Journal |
| issn | 1367-2630 |
| language | English |
| last_indexed | 2024-03-08T13:45:17Z |
| publishDate | 2024-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | New Journal of Physics |
| spelling | doaj.art-4029721104764fd8b135669e2cc6bc792024-01-16T09:26:00ZengIOP PublishingNew Journal of Physics1367-26302024-01-0126101302710.1088/1367-2630/ad106aHigher-order random network modelsJinyu Huang0https://orcid.org/0000-0001-8237-6324Youxin Hu1Weifu Li2Maoyan Lin3School of Computer Science and Engineering, Sichuan University of Science and Engineering , Zigong, Sichuan 643002, People’s Republic of ChinaSchool of Computer Science and Engineering, Sichuan University of Science and Engineering , Zigong, Sichuan 643002, People’s Republic of ChinaSchool of Computer Science and Engineering, Sichuan University of Science and Engineering , Zigong, Sichuan 643002, People’s Republic of ChinaSchool of Computer Science and Engineering, Sichuan University of Science and Engineering , Zigong, Sichuan 643002, People’s Republic of ChinaMost existing random network models that describe complex systems in nature and society are developed through connections that indicate a binary relationship between two nodes. However, real-world networks are so complicated that we can only identify many critical hidden structural properties through higher-order structures such as network motifs. Here we propose a framework in which we define higher-order stubs, higher-order degrees, and generating functions for developing higher-order complex network models. Then we develop higher-order random networks with arbitrary higher-order degree distributions. The developed higher-order random networks share critical structural properties with real-world networks, but traditional connection-based random networks fail to exhibit these structural properties. For example, as opposed to connection-based random network models, the proposed higher-order random network models can generate networks with power-law higher-order degree distributions, right-skewed degree distributions, and high average clustering coefficients simultaneously. These properties are also observed on the Internet, the Amazon product co-purchasing network, and collaboration networks. Thus, the proposed higher-order random networks are necessary supplements to traditional connection-based random networks.https://doi.org/10.1088/1367-2630/ad106arandom graphshigher-order structurecomplex networksnetwork motif |
| spellingShingle | Jinyu Huang Youxin Hu Weifu Li Maoyan Lin Higher-order random network models New Journal of Physics random graphs higher-order structure complex networks network motif |
| title | Higher-order random network models |
| title_full | Higher-order random network models |
| title_fullStr | Higher-order random network models |
| title_full_unstemmed | Higher-order random network models |
| title_short | Higher-order random network models |
| title_sort | higher order random network models |
| topic | random graphs higher-order structure complex networks network motif |
| url | https://doi.org/10.1088/1367-2630/ad106a |
| work_keys_str_mv | AT jinyuhuang higherorderrandomnetworkmodels AT youxinhu higherorderrandomnetworkmodels AT weifuli higherorderrandomnetworkmodels AT maoyanlin higherorderrandomnetworkmodels |