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On analysis of entropy measure via logarithmic regression model for 2D-honeycomb networks

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Abstract

A well-known structure in materials science and nanotechnology, the 2D-Honeycomb network has unique topological features and prospective uses. In this study, we introduce and explore newly defined Zagreb indices designed exclusively for 2D-Honeycomb Networks as we dig into the world of complicated network research. We investigate the links between these indices and entropy measures through a thorough analysis, shedding insight into the complex interaction between structural features and information content. In addition, we use a logarithmic regression model to reveal the network’s underlying patterns. Our research advances knowledge of 2D-honeycomb networks and illustrates the utility of logarithmic regression in complex system modeling.

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Acknowledgements

This research work is supported by the project: “Mathematical Modeling and Intelligent Computing Research Center” with grant number(GKY-2022CQJG-1)

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Authors and Affiliations

  1. College of General Education, Guangdong University of Science and Technology, Guangdong, 523083, China

    Caicai Feng

  2. Abdus Salam School of Mathematical Sciences, Government College University, Lahore, Pakistan

    Muhammad Farhan Hanif

  3. Department of Mathematics, COMSATS University Islamabad, Lahore Campus, Lahore, Pakistan

    Muhammad Kamran Siddiqui, Mazhar Hussain & Nazir Hussain

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  1. Caicai Feng
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Correspondence to Muhammad Kamran Siddiqui.

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Feng, C., Hanif, M.F., Siddiqui, M.K. et al. On analysis of entropy measure via logarithmic regression model for 2D-honeycomb networks. Eur. Phys. J. Plus 138, 924 (2023). https://doi.org/10.1140/epjp/s13360-023-04547-4

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