Directed clustering in weighted networks: A new perspective.
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| Title: | Directed clustering in weighted networks: A new perspective. |
|---|---|
| Authors: | Clemente, G.P.1 gianpaolo.clemente@unicatt.it, Grassi, R.2 rosanna.grassi@unimib.it |
| Source: | Chaos, Solitons & Fractals. Feb2018, Vol. 107, p26-38. 13p. |
| Subjects: | Coefficients (Statistics), Cluster analysis (Statistics), Directed graphs, Weighted graphs, Weighted residual method |
| Abstract: | Several definitions of clustering coefficient for weighted networks have been proposed in literature, but less attention has been paid to both weighted and directed networks. We provide a new local clustering coefficient for this kind of networks, starting from those already existing in the literature for the weighted and undirected case. Furthermore, we extract from our coefficient four specific components, in order to separately consider different link patterns of triangles. Empirical applications on several real networks from different frameworks and with different order are provided. The performance of our coefficient is also compared with that of existing coefficients. [ABSTRACT FROM AUTHOR] |
| Copyright of Chaos, Solitons & Fractals is the property of Pergamon Press - An Imprint of Elsevier Science 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 | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 127761564 AccessLevel: 6 PubType: Periodical PubTypeId: serialPeriodical PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Directed clustering in weighted networks: A new perspective. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Clemente%2C+G%2EP%2E%22">Clemente, G.P.</searchLink><relatesTo>1</relatesTo><i> gianpaolo.clemente@unicatt.it</i><br /><searchLink fieldCode="AR" term="%22Grassi%2C+R%2E%22">Grassi, R.</searchLink><relatesTo>2</relatesTo><i> rosanna.grassi@unimib.it</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Chaos%2C+Solitons+%26+Fractals%22">Chaos, Solitons & Fractals</searchLink>. Feb2018, Vol. 107, p26-38. 13p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Coefficients+%28Statistics%29%22">Coefficients (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Cluster+analysis+%28Statistics%29%22">Cluster analysis (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Directed+graphs%22">Directed graphs</searchLink><br /><searchLink fieldCode="DE" term="%22Weighted+graphs%22">Weighted graphs</searchLink><br /><searchLink fieldCode="DE" term="%22Weighted+residual+method%22">Weighted residual method</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Several definitions of clustering coefficient for weighted networks have been proposed in literature, but less attention has been paid to both weighted and directed networks. We provide a new local clustering coefficient for this kind of networks, starting from those already existing in the literature for the weighted and undirected case. Furthermore, we extract from our coefficient four specific components, in order to separately consider different link patterns of triangles. Empirical applications on several real networks from different frameworks and with different order are provided. The performance of our coefficient is also compared with that of existing coefficients. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Chaos, Solitons & Fractals is the property of Pergamon Press - An Imprint of Elsevier Science 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: 10.1016/j.chaos.2017.12.007 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 26 Subjects: – SubjectFull: Coefficients (Statistics) Type: general – SubjectFull: Cluster analysis (Statistics) Type: general – SubjectFull: Directed graphs Type: general – SubjectFull: Weighted graphs Type: general – SubjectFull: Weighted residual method Type: general Titles: – TitleFull: Directed clustering in weighted networks: A new perspective. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Clemente, G.P. – PersonEntity: Name: NameFull: Grassi, R. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2018 Type: published Y: 2018 Identifiers: – Type: issn-print Value: 09600779 Numbering: – Type: volume Value: 107 Titles: – TitleFull: Chaos, Solitons & Fractals Type: main |
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