https://doi.org/10.1140/epjp/s13360-023-04547-4
Regular Article
On analysis of entropy measure via logarithmic regression model for 2D-honeycomb networks
1
College of General Education, Guangdong University of Science and Technology, 523083, Guangdong, China
2
Abdus Salam School of Mathematical Sciences, Government College University, Lahore, Pakistan
3
Department of Mathematics, COMSATS University Islamabad, Lahore Campus, Lahore, Pakistan
Received:
31
August
2023
Accepted:
3
October
2023
Published online:
18
October
2023
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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© The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
