2016
DOI: 10.1016/j.dam.2016.02.019
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The normalized Laplacians, degree-Kirchhoff index and the spanning trees of linear hexagonal chains

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Cited by 62 publications
(44 citation statements)
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“…Huang and Li obtained the following decomposition theorem of the normalized Laplacian characteristic polynomial. This is an exact analogue of the decomposition theorem of the Laplacian characteristic polynomial …”
Section: Normalized Laplacian Characteristic Polynomial and Some Prelmentioning
confidence: 99%
See 1 more Smart Citation
“…Huang and Li obtained the following decomposition theorem of the normalized Laplacian characteristic polynomial. This is an exact analogue of the decomposition theorem of the Laplacian characteristic polynomial …”
Section: Normalized Laplacian Characteristic Polynomial and Some Prelmentioning
confidence: 99%
“…Hence, it is interesting to obtain the closed‐form formula for the multiplicative degree‐Kirchhoff index of graph G . For recent advances on this topic, one may be referred to and the references with in.…”
Section: Introductionmentioning
confidence: 99%
“…This is an exact analogue of the decomposition theorem of the Laplacian characteristic polynomial Theorem Let G be a graph, and let ℒ( G ), ℒ A ( G ), ℒ S ( G ) be defined as above. Then, Ψ(G; x) = Φ(A(G)) Φ(S(G)). Lemma Let G be an n‐vertex connected graph with m edges, and let 0 = λ 1 < λ 2 ≤ ⋯ ≤ λ n be the eigenvalues of ℒ(G) .…”
Section: Normalized Laplacian Characteristic Polynomial and Some Prelmentioning
confidence: 99%
“…Recently, some closed‐form formulas for the multiplicative degree‐Kirchhoff index have been obtained for some types of graphs. One may be referred to References for detailed information.…”
Section: Introductionmentioning
confidence: 99%
“…(For an example of the very latest work on spanning trees and their relations to graph eigenvalues, please see Refs. [51,52] and the articles cited in them.) Let D be the (v × v) degree matrix [27,53,54] of some labelled graph G (with v vertices and e edges), and let A be the (v × v) vertex-adjacency matrix of G, so labelled.…”
Section: Introductionmentioning
confidence: 99%