2009
DOI: 10.13001/1081-3810.1308
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The distance matrix of a bidirected tree

Abstract: Abstract. A bidirected tree is a tree in which each edge is replaced by two arcs in either direction. Formulas are obtained for the determinant and the inverse of a bidirected tree, generalizing well-known formulas in the literature.

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Cited by 13 publications
(18 citation statements)
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“…It may be remarked that an additive analogue of Theorem 2.2 has been considered by Bapat, Lal and Pati (see [2]). For this, suppose we bidirect the edges of a tree and have weights w(a) for each arc a ∈ A.…”
Section: Elamentioning
confidence: 93%
See 1 more Smart Citation
“…It may be remarked that an additive analogue of Theorem 2.2 has been considered by Bapat, Lal and Pati (see [2]). For this, suppose we bidirect the edges of a tree and have weights w(a) for each arc a ∈ A.…”
Section: Elamentioning
confidence: 93%
“…For this, suppose we bidirect the edges of a tree and have weights w(a) for each arc a ∈ A. Define the distance between vertices i, j of the tree by replacing the product in (2.1) by the sum: [2]. The formula is fairly complicated, but when q e = q, t e = t for all e ∈ E(T ), it gets considerably simplified.…”
Section: Elamentioning
confidence: 99%
“…The above expression gives a formula for the inverse of the distance matrix of a tree in terms of the Laplacian matrix. The determinant and the inverse of the distance matrix were also studied for bi-directed trees and weighted trees (for details, see [3,10]). In [2], similar results were studied for q-analogue of the distance ma- trix, which is a generalization of the distance matrix for a tree.…”
Section: Introductionmentioning
confidence: 99%
“…The above expression gives formula for the inverse of distance matrix of a tree in terms of the Laplacian matrix. Several extensions and generalization of this result have been studied in [3,4,7,14,15,16,18,19]. The primary objective of these extensions is to define a matrix L called Laplacian-like matrix satisfying L1 = 0 and 1 t L = 0 and find the inverse of the distance matrix D(G) in the following form:…”
Section: Introductionmentioning
confidence: 99%