Kendall rank correlation analysis of Malatya Centrality Algorithm with well-known centrality measures.
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| Title: | Kendall rank correlation analysis of Malatya Centrality Algorithm with well-known centrality measures. |
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| Authors: | YAKUT, Selman1 selman.yakut@inonu.edu.tr, ÖZTEMİZ, Furkan1, KARCI, Ali1 |
| Source: | Sigma: Journal of Engineering & Natural Sciences / Mühendislik ve Fen Bilimleri Dergisi. Aug2025, Vol. 43 Issue 4, p1339-1354. 16p. |
| Subjects: | Rank correlation (Statistics), Graph theory, Communication network analysis |
| Abstract: | The concept of centrality is widely used in graph theory to determine the dominance of nodes within a graph. This concept is crucial for solving many real-life problems that are modeled using graphs. In this study, the effectiveness of a new approach, the Malatya Centrality Algorithm, for determining the centrality of nodes in a graph is examined. This algorithm provides effective solutions to problems in both graph theory and real-life applications. The centrality value in the Malatya Centrality Algorithm is calculated by summing the ratios of the degree of the relevant node to the degrees of its neighboring nodes. To demonstrate the effectiveness of the Malatya Centrality Algorithm, comparisons and analyses were conducted with well-known centrality algorithms in the literature. Various types of graphs, including random graphs, benchmark graphs, social network graphs, and lattice bipartite graphs, were used for these comparisons and analyses. Kendall rank correlation analysis and tests were performed on these different types of graphs for the Malatya Centrality Algorithm and the well-known centrality measures in the literature. The tests conducted on various graphs reveal the ranking of nodes based on their effectiveness. These rankings help identify nodes used in numerous problems. The tests and analyses demonstrate that the Malatya Centrality Algorithm produces results similar to those of established centrality algorithms in the literature and confirms its effectiveness across different types of graphs. [ABSTRACT FROM AUTHOR] |
| Copyright of Sigma: Journal of Engineering & Natural Sciences / Mühendislik ve Fen Bilimleri Dergisi is the property of Sigma: Journal of Engineering & Natural Sciences / Mühendislik ve Fen Bilimleri Dergisi and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 188227795 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Kendall rank correlation analysis of Malatya Centrality Algorithm with well-known centrality measures. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22YAKUT%2C+Selman%22">YAKUT, Selman</searchLink><relatesTo>1</relatesTo><i> selman.yakut@inonu.edu.tr</i><br /><searchLink fieldCode="AR" term="%22ÖZTEMİZ%2C+Furkan%22">ÖZTEMİZ, Furkan</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22KARCI%2C+Ali%22">KARCI, Ali</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Sigma%3A+Journal+of+Engineering+%26+Natural+Sciences+%2F+Mühendislik+ve+Fen+Bilimleri+Dergisi%22">Sigma: Journal of Engineering & Natural Sciences / Mühendislik ve Fen Bilimleri Dergisi</searchLink>. Aug2025, Vol. 43 Issue 4, p1339-1354. 16p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Rank+correlation+%28Statistics%29%22">Rank correlation (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Graph+theory%22">Graph theory</searchLink><br /><searchLink fieldCode="DE" term="%22Communication+network+analysis%22">Communication network analysis</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The concept of centrality is widely used in graph theory to determine the dominance of nodes within a graph. This concept is crucial for solving many real-life problems that are modeled using graphs. In this study, the effectiveness of a new approach, the Malatya Centrality Algorithm, for determining the centrality of nodes in a graph is examined. This algorithm provides effective solutions to problems in both graph theory and real-life applications. The centrality value in the Malatya Centrality Algorithm is calculated by summing the ratios of the degree of the relevant node to the degrees of its neighboring nodes. To demonstrate the effectiveness of the Malatya Centrality Algorithm, comparisons and analyses were conducted with well-known centrality algorithms in the literature. Various types of graphs, including random graphs, benchmark graphs, social network graphs, and lattice bipartite graphs, were used for these comparisons and analyses. Kendall rank correlation analysis and tests were performed on these different types of graphs for the Malatya Centrality Algorithm and the well-known centrality measures in the literature. The tests conducted on various graphs reveal the ranking of nodes based on their effectiveness. These rankings help identify nodes used in numerous problems. The tests and analyses demonstrate that the Malatya Centrality Algorithm produces results similar to those of established centrality algorithms in the literature and confirms its effectiveness across different types of graphs. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Sigma: Journal of Engineering & Natural Sciences / Mühendislik ve Fen Bilimleri Dergisi is the property of Sigma: Journal of Engineering & Natural Sciences / Mühendislik ve Fen Bilimleri Dergisi and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.14744/sigma.2025.00124 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 1339 Subjects: – SubjectFull: Rank correlation (Statistics) Type: general – SubjectFull: Graph theory Type: general – SubjectFull: Communication network analysis Type: general Titles: – TitleFull: Kendall rank correlation analysis of Malatya Centrality Algorithm with well-known centrality measures. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: YAKUT, Selman – PersonEntity: Name: NameFull: ÖZTEMİZ, Furkan – PersonEntity: Name: NameFull: KARCI, Ali IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: Aug2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 13047191 Numbering: – Type: volume Value: 43 – Type: issue Value: 4 Titles: – TitleFull: Sigma: Journal of Engineering & Natural Sciences / Mühendislik ve Fen Bilimleri Dergisi Type: main |
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