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<Article>
<Journal>
				<PublisherName>University of Kashan</PublisherName>
				<JournalTitle>Iranian Journal of Mathematical Chemistry</JournalTitle>
				<Issn>2228-6489</Issn>
				<Volume>15</Volume>
				<Issue>2</Issue>
				<PubDate PubStatus="epublish">
					<Year>2024</Year>
					<Month>06</Month>
					<Day>01</Day>
				</PubDate>
			</Journal>
<ArticleTitle>The Laplacian Spectrum of the Generalized $n$-Prism Networks</ArticleTitle>
<VernacularTitle></VernacularTitle>
			<FirstPage>65</FirstPage>
			<LastPage>78</LastPage>
			<ELocationID EIdType="pii">114367</ELocationID>
			
<ELocationID EIdType="doi">10.22052/ijmc.2023.253926.1789</ELocationID>
			
			<Language>EN</Language>
<AuthorList>
<Author>
					<FirstName>Mehdi</FirstName>
					<LastName>Eliasi</LastName>
<Affiliation>Department of Mathematics, Khansar Faculty, University of Isfahan, Iran</Affiliation>

</Author>
</AuthorList>
				<PublicationType>Journal Article</PublicationType>
			<History>
				<PubDate PubStatus="received">
					<Year>2023</Year>
					<Month>12</Month>
					<Day>01</Day>
				</PubDate>
			</History>
		<Abstract>‎The Laplacian eigenvalues and polynomials of the networks play an essential role in understanding the relations between the topology and the dynamic of networks‎. ‎Generally‎, ‎computation of the Laplacian spectrum of a network is a hard problem and there are just a few classes of graphs with the property that their spectra have been completely computed‎. ‎Laplacian spectrum for $ n$-prism networks was investigated in [Liu et al.‎, ‎Neurocomputing 198 (2016) 69-73]‎. ‎In this paper‎, ‎we give a method for calculating the eigenvalues and characteristic polynomial of the Laplacian matrix of a generalized $n$-prism network‎. ‎We show how such large networks can be constructed from small graphs by using graph products‎. ‎Moreover‎, ‎our results are used to obtain the Kirchhoff index and the number of the spanning trees in the generalized $n$-prism networks‎. ‎We also give some examples of applications‎, ‎that explain the usefulness and efficiency of the proposed method‎.</Abstract>
		<ObjectList>
			<Object Type="keyword">
			<Param Name="value">Laplacian spectra</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Prism network</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Spanning tree‎</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Kirchhoff index</Param>
			</Object>
		</ObjectList>
<ArchiveCopySource DocType="pdf">https://ijmc.kashanu.ac.ir/article_114367_3482630c7f9fda53ba024ac03095bc72.pdf</ArchiveCopySource>
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