Results 11 to 20 of about 6,673 (197)

On the Aα-Eigenvalues of Signed Graphs

open access: yesMathematics, 2021
For α∈[0,1], let Aα(Gσ)=αD(G)+(1−α)A(Gσ), where G is a simple undirected graph, D(G) is the diagonal matrix of its vertex degrees and A(Gσ) is the adjacency matrix of the signed graph Gσ whose underlying graph is G.
Germain Pastén, Oscar Rojo, Luis Medina
doaj   +1 more source

Graphs Whose Aα -Spectral Radius Does Not Exceed 2

open access: yesDiscussiones Mathematicae Graph Theory, 2020
Let A(G) and D(G) be the adjacency matrix and the degree matrix of a graph G, respectively. For any real α ∈ [0, 1], we consider Aα (G) = αD(G) + (1 − α)A(G) as a graph matrix, whose largest eigenvalue is called the Aα -spectral radius of G.
Wang Jian Feng   +3 more
doaj   +1 more source

Aα-Spectral Characterizations of Some Joins

open access: yesJournal of Mathematics, 2020
Let G be a graph with n vertices. For every real α∈0,1, write AαG for the matrix AαG=αDG+1−αAG, where AG and DG denote the adjacency matrix and the degree matrix of G, respectively.
Tingzeng Wu, Tian Zhou
doaj   +1 more source

Directed random geometric graphs: structural and spectral properties

open access: yesJournal of Physics: Complexity, 2022
In this work we analyze structural and spectral properties of a model of directed random geometric graphs: given n vertices uniformly and independently distributed on the unit square, a directed edge is set between two vertices if their distance is ...
Kevin Peralta-Martinez   +1 more
doaj   +1 more source

On the Aα-Spectral Radii of Cactus Graphs

open access: yesMathematics, 2020
Let A ( G ) be the adjacent matrix and D ( G ) the diagonal matrix of the degrees of a graph G, respectively. For 0 ≤ α ≤ 1 , the A α -matrix is the general adjacency and signless Laplacian spectral matrix having the form of
Chunxiang Wang   +3 more
doaj   +1 more source

Graphs with Clusters Perturbed by Regular Graphs—Aα-Spectrum and Applications

open access: yesDiscussiones Mathematicae Graph Theory, 2020
Given a graph G, its adjacency matrix A(G) and its diagonal matrix of vertex degrees D(G), consider the matrix Aα (G) = αD(G) + (1 − α)A(G), where α ∈ [0, 1).
Cardoso Domingos M.   +2 more
doaj   +1 more source

图的Aα-特征多项式系数的一个注记(A note on the coefficients of the Aα-characteristic polynomial of a graph)

open access: yesZhejiang Daxue xuebao. Lixue ban, 2019
Let G be a graph on n vertices, and let A( G ) and D ( G ) denote the adjacency matrix and the degree matrix of G, respectively. Define Aα ( G )= αD ( G )+( 1 - α ) A( G ) for any real α ∈ [ 0,1 ].
LIUShunyi,(柳顺义)   +1 more
doaj   +1 more source

On the Number of α-Labeled Graphs

open access: yesDiscussiones Mathematicae Graph Theory, 2018
When a graceful labeling of a bipartite graph places the smaller labels in one of the stable sets of the graph, it becomes an α-labeling. This is the most restrictive type of difference-vertex labeling and it is located at the very core of this research ...
Barrientos Christian, Minion Sarah
doaj   +1 more source

The Caenorhabditis elegans DPF‐3 and human DPP4 have tripeptidyl peptidase activity

open access: yesFEBS Letters, EarlyView.
The dipeptidyl peptidase IV (DPPIV) family comprises serine proteases classically defined by their ability to remove dipeptides from the N‐termini of substrates, a feature that gave the family its name. Here, we report the discovery of a previously unrecognized tripeptidyl peptidase activity in DPPIV family members from two different species.
Aditya Trivedi, Rajani Kanth Gudipati
wiley   +1 more source

Mechanisms of parasite‐mediated disruption of brain vessels

open access: yesFEBS Letters, EarlyView.
Parasites can affect the blood vessels of the brain, often causing serious neurological problems. This review explains how different parasites interact with and disrupt these vessels, what this means for brain health, and why these processes matter. Understanding these mechanisms may help us develop better ways to prevent or treat brain infections in ...
Leonor Loira   +3 more
wiley   +1 more source

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