https://doi.org/10.1140/epjp/s13360-023-04441-z
Regular Article
Insights into network properties: spectrum-based analysis with Laplacian and signless Laplacian spectra
Department of Mathematics, University of the Punjab, Lahore, Pakistan
Received:
25
July
2023
Accepted:
1
September
2023
Published online:
13
September
2023
The article explores the utilizations of Laplacian and signless Laplacian spectra across diverse fields such as theoretical chemistry, computer science, electrical networks, and complex networks. These spectra offer valuable insights into real-world network structures and aid in predicting chemical substance properties. The study primarily employs spectrum-based analysis of numerous graph such as vertex duplication, edge duplication and m-duplication which are planar and polyhedral. These evaluated spectra then inform calculations of network measures like mean-first passage time, path length, spanning trees, and spectral radius. The research enhances our grasp of the relationship between graph spectra and network characteristics, contributing to a comprehensive understanding of complex networks and enabling predictions and analyses across scientific disciplines.
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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.
