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Article . 2023 . Peer-reviewed
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Article . 2023
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Article . 2023
Data sources: DBLP
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The Clustering Coefficient for Graph Products

Authors: Jhon J. Aguilar-Alarcón; Juan Carlos Hernández-Gómez; Jesús Romero-Valencia;

The Clustering Coefficient for Graph Products

Abstract

The clustering coefficient of a vertex v, of degree at least 2, in a graph Γ is obtained using the formula C(v)=2t(v)deg(v)(deg(v)−1), where t(v) denotes the number of triangles of the graph containing v as a vertex, and the clustering coefficient of Γ is defined as the average of the clustering coefficient of all vertices of Γ, that is, C(Γ)=1|V|∑v∈VC(v), where V is the vertex set of the graph. In this paper, we give explicit expressions for the clustering coefficient of corona and lexicographic products, as well as for the Cartesian sum; such expressions are given in terms of the order and size of factors, and the degree and number of triangles of vertices in each factor.

Related Organizations
Keywords

QA1-939, clustering coefficient, corona product, Cartesian sum, lexicographic product, Mathematics, graph product

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
5
Top 10%
Average
Top 10%
gold