2016
DOI: 10.1080/00927872.2015.1065866
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Nonzero Component Graph of a Finite Dimensional Vector Space

Abstract: In this paper, we introduce a graph structure, called non-zero component graph on finite dimensional vector spaces. We show that the graph is connected and find its domination number and independence number. We also study the inter-relationship between vector space isomorphisms and graph isomorphisms and it is shown that two graphs are isomorphic if and only if the corresponding vector spaces are so. Finally, we determine the degree of each vertex in case the base field is finite.

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Cited by 50 publications
(14 citation statements)
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“…A binary relation on a vector space, group, or ring can be studied with the help of considering the associating graph defined by this relation. Symplectic graphs, orthogonal graphs, subspace inclusion graphs, and nonzero component graphs are such examples which have been considered recently (see [1][2][3][4]).…”
Section: Introductionmentioning
confidence: 99%
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“…A binary relation on a vector space, group, or ring can be studied with the help of considering the associating graph defined by this relation. Symplectic graphs, orthogonal graphs, subspace inclusion graphs, and nonzero component graphs are such examples which have been considered recently (see [1][2][3][4]).…”
Section: Introductionmentioning
confidence: 99%
“…All graphs considered in this paper are undirected simple graphs (without loops and multiedges). e nonzero component graph Γ c (V) of V, introduced by Das [1], is a graph with vertex set V * � V\ 0 { }, in which for distinct α, β ∈ V * , there exists an edge joining α and β if and only if S A (α) ∩ S A (β) ≠ ∅. In [1], the author studied the diameter, dominating number, and dependent number of Γ c (V).…”
Section: Introductionmentioning
confidence: 99%
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“…A vector is a geometric object that has both a magnitude and a direction (Sentosa, 2016). Vector space are finite dimensional over a field and dim ( ) nv  (Das, 2016a). In addition to vector space, this study uses cosine similarity.…”
Section: Introductionmentioning
confidence: 99%