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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.
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]
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Database: Engineering Source
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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]
ISSN:17445760
DOI:10.1080/17445760.2024.2427194