Analysis and synthesis of a growing network model generating dense scale-free networks via category theory
Abstract We propose a growing network model that can generate dense scale-free networks with an almost neutral degree−degree correlation and a negative scaling of local clustering coefficient. The model is obtained by modifying an existing model in the literature that can also generate dense scale-f...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2020-12-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-020-79318-7 |
| _version_ | 1818752395914510336 |
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| author | Taichi Haruna Yukio-Pegio Gunji |
| author_facet | Taichi Haruna Yukio-Pegio Gunji |
| author_sort | Taichi Haruna |
| collection | DOAJ |
| description | Abstract We propose a growing network model that can generate dense scale-free networks with an almost neutral degree−degree correlation and a negative scaling of local clustering coefficient. The model is obtained by modifying an existing model in the literature that can also generate dense scale-free networks but with a different higher-order network structure. The modification is mediated by category theory. Category theory can identify a duality structure hidden in the previous model. The proposed model is built so that the identified duality is preserved. This work is a novel application of category theory for designing a network model focusing on a universal algebraic structure. |
| first_indexed | 2024-12-18T04:50:47Z |
| format | Article |
| id | doaj.art-0bc10a92fd71473e9993bb9618b71509 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-18T04:50:47Z |
| publishDate | 2020-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-0bc10a92fd71473e9993bb9618b715092022-12-21T21:20:26ZengNature PortfolioScientific Reports2045-23222020-12-011011810.1038/s41598-020-79318-7Analysis and synthesis of a growing network model generating dense scale-free networks via category theoryTaichi Haruna0Yukio-Pegio Gunji1Department of Information and Sciences, School of Arts and Sciences, Tokyo Woman’s Christian UniversityDepartment of Intermedia Art and Science, School of Fundamental Science and Technology, Waseda UniversityAbstract We propose a growing network model that can generate dense scale-free networks with an almost neutral degree−degree correlation and a negative scaling of local clustering coefficient. The model is obtained by modifying an existing model in the literature that can also generate dense scale-free networks but with a different higher-order network structure. The modification is mediated by category theory. Category theory can identify a duality structure hidden in the previous model. The proposed model is built so that the identified duality is preserved. This work is a novel application of category theory for designing a network model focusing on a universal algebraic structure.https://doi.org/10.1038/s41598-020-79318-7 |
| spellingShingle | Taichi Haruna Yukio-Pegio Gunji Analysis and synthesis of a growing network model generating dense scale-free networks via category theory Scientific Reports |
| title | Analysis and synthesis of a growing network model generating dense scale-free networks via category theory |
| title_full | Analysis and synthesis of a growing network model generating dense scale-free networks via category theory |
| title_fullStr | Analysis and synthesis of a growing network model generating dense scale-free networks via category theory |
| title_full_unstemmed | Analysis and synthesis of a growing network model generating dense scale-free networks via category theory |
| title_short | Analysis and synthesis of a growing network model generating dense scale-free networks via category theory |
| title_sort | analysis and synthesis of a growing network model generating dense scale free networks via category theory |
| url | https://doi.org/10.1038/s41598-020-79318-7 |
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