A new strategy to generate scale free networks by capturing the information processing mechanism in a specific family of elementary cellular automata.
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| Title: | A new strategy to generate scale free networks by capturing the information processing mechanism in a specific family of elementary cellular automata. |
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| Authors: | Nayyeri, Amirahmad1 (AUTHOR), Mansoori, Eghbal G.1 (AUTHOR), Taheri, Mohammad1 (AUTHOR) motaheri@shirazu.ac.ir |
| Source: | International Journal of Parallel, Emergent & Distributed Systems. Jan2026, Vol. 41 Issue 1, p34-51. 18p. |
| Subjects: | Scale-free network (Statistical physics), Cellular automata, Distribution (Probability theory), Information processing, System dynamics, Statistical correlation, Maximum entropy method |
| Abstract: | The study of complex networks has garnered significant attention in recent years due to their ability to visualize intricate systems. However, understanding the fundamental structures underlying their formation dynamics has remained elusive due to the enigmatic interplay between local and global properties. A hallmark of real-world complex networks is the presence of power-law distributions, which account for many of their intriguing characteristics. Preferential attachment has emerged as a common mechanism for generating scale-free networks. This research introduces a novel strategy for creating scale-free networks by capturing the spatiotemporal properties of an Elementary Cellular Automata trajectory. The process has been validated through the analysis of information-theoretic measures such as correlation information and entropy rate. Our findings demonstrate how the system's sub-linear movement towards entropy reduction and increased correlations leads to state space localization, manifesting as a power-law distribution in the resulting network. The proposed method implicitly offers various forms of preferential attachment (sub-linear, linear, and super-linear) based on the sampling time pattern, circumventing the need for the first-mover advantage to generate hubs and requiring no global system information. We evaluate the resulting network using well-established criteria. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Parallel, Emergent & Distributed Systems is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 190575063 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A new strategy to generate scale free networks by capturing the information processing mechanism in a specific family of elementary cellular automata. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Nayyeri%2C+Amirahmad%22">Nayyeri, Amirahmad</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Mansoori%2C+Eghbal+G%2E%22">Mansoori, Eghbal G.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Taheri%2C+Mohammad%22">Taheri, Mohammad</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> motaheri@shirazu.ac.ir</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Parallel%2C+Emergent+%26+Distributed+Systems%22">International Journal of Parallel, Emergent & Distributed Systems</searchLink>. Jan2026, Vol. 41 Issue 1, p34-51. 18p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Scale-free+network+%28Statistical+physics%29%22">Scale-free network (Statistical physics)</searchLink><br /><searchLink fieldCode="DE" term="%22Cellular+automata%22">Cellular automata</searchLink><br /><searchLink fieldCode="DE" term="%22Distribution+%28Probability+theory%29%22">Distribution (Probability theory)</searchLink><br /><searchLink fieldCode="DE" term="%22Information+processing%22">Information processing</searchLink><br /><searchLink fieldCode="DE" term="%22System+dynamics%22">System dynamics</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+correlation%22">Statistical correlation</searchLink><br /><searchLink fieldCode="DE" term="%22Maximum+entropy+method%22">Maximum entropy method</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The study of complex networks has garnered significant attention in recent years due to their ability to visualize intricate systems. However, understanding the fundamental structures underlying their formation dynamics has remained elusive due to the enigmatic interplay between local and global properties. A hallmark of real-world complex networks is the presence of power-law distributions, which account for many of their intriguing characteristics. Preferential attachment has emerged as a common mechanism for generating scale-free networks. This research introduces a novel strategy for creating scale-free networks by capturing the spatiotemporal properties of an Elementary Cellular Automata trajectory. The process has been validated through the analysis of information-theoretic measures such as correlation information and entropy rate. Our findings demonstrate how the system's sub-linear movement towards entropy reduction and increased correlations leads to state space localization, manifesting as a power-law distribution in the resulting network. The proposed method implicitly offers various forms of preferential attachment (sub-linear, linear, and super-linear) based on the sampling time pattern, circumventing the need for the first-mover advantage to generate hubs and requiring no global system information. We evaluate the resulting network using well-established criteria. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Parallel, Emergent & Distributed Systems is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/17445760.2024.2427194 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 18 StartPage: 34 Subjects: – SubjectFull: Scale-free network (Statistical physics) Type: general – SubjectFull: Cellular automata Type: general – SubjectFull: Distribution (Probability theory) Type: general – SubjectFull: Information processing Type: general – SubjectFull: System dynamics Type: general – SubjectFull: Statistical correlation Type: general – SubjectFull: Maximum entropy method Type: general Titles: – TitleFull: A new strategy to generate scale free networks by capturing the information processing mechanism in a specific family of elementary cellular automata. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Nayyeri, Amirahmad – PersonEntity: Name: NameFull: Mansoori, Eghbal G. – PersonEntity: Name: NameFull: Taheri, Mohammad IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 17445760 Numbering: – Type: volume Value: 41 – Type: issue Value: 1 Titles: – TitleFull: International Journal of Parallel, Emergent & Distributed Systems Type: main |
| ResultId | 1 |
