Results 21 to 30 of about 13,572 (222)

Spectral properties for the Laplacian of a generalized Wigner matrix [PDF]

open access: yesRandom Matrices: Theory and Applications, 2021
In this paper, we consider the spectrum of a Laplacian matrix, also known as Markov matrices where the entries of the matrix are independent but have a variance profile. Motivated by recent works on generalized Wigner matrices we assume that the variance profile gives rise to a sequence of graphons.
Chatterjee, A., Hazra, R.S.
openaire   +4 more sources

The Characterizing Properties of (Signless) Laplacian Permanental Polynomials of Almost Complete Graphs

open access: yesJournal of Mathematics, 2021
Let G be a graph with n vertices, and let LG and QG denote the Laplacian matrix and signless Laplacian matrix, respectively. The Laplacian (respectively, signless Laplacian) permanental polynomial of G is defined as the permanent of the characteristic ...
Tingzeng Wu, Tian Zhou
doaj   +1 more source

Forest matrices around the Laplacian matrix [PDF]

open access: yesLinear Algebra and its Applications, 2002
19 pages, presented at the Edinburgh (2001) Conference on Algebraic Graph ...
Rafig Agaev, Pavel Chebotarev
openaire   +4 more sources

Random matrix analysis of network Laplacians [PDF]

open access: yesPhysica A: Statistical Mechanics and its Applications, 2008
We analyze eigenvalues fluctuations of the Laplacian of various networks under the random matrix theory framework. Analyses of random networks, scale-free networks and small-world networks show that nearest neighbor spacing distribution of the Laplacian of these networks follow Gaussian orthogonal ensemble statistics of random matrix theory ...
Jalan, S., Bandyopadhyay, J.
openaire   +4 more sources

The gamma-Signless Laplacian Adjacency Matrix of Mixed Graphs

open access: yesTheory and Applications of Graphs, 2023
The α-Hermitian adjacency matrix Hα of a mixed graph X has been recently introduced. It is a generalization of the adjacency matrix of unoriented graphs. In this paper, we consider a special case of the complex number α.
Omar Alomari   +2 more
doaj   +1 more source

Computing the Permanent of the Laplacian Matrices of Nonbipartite Graphs

open access: yesJournal of Mathematics, 2021
Let G be a graph with Laplacian matrix LG. Denote by per LG the permanent of LG. In this study, we investigate the problem of computing the permanent of the Laplacian matrix of nonbipartite graphs.
Xiaoxue Hu, Grace Kalaso
doaj   +1 more source

On graphs with distance Laplacian eigenvalues of multiplicity n−4

open access: yesAKCE International Journal of Graphs and Combinatorics, 2023
Let G be a connected simple graph with n vertices. The distance Laplacian matrix [Formula: see text] is defined as [Formula: see text], where [Formula: see text] is the diagonal matrix of vertex transmissions and [Formula: see text] is the distance ...
Saleem Khan, S. Pirzada, A. Somasundaram
doaj   +1 more source

Monophonic Distance Laplacian Energy of Transformation Graphs Sn^++-,Sn^{+-+},Sn^{+++}

open access: yesRatio Mathematica, 2023
Let $G$ be a simple connected graph of order $n$, $v_{i}$ its vertex. Let $\delta^{L}_{1}, \delta^{L}_{2}, \ldots, \delta^{L}_{n}$ be the eigenvalues of the distance Laplacian matrix $D^{L}$ of $G$. The distance Laplacian energy is denoted by $LE_{D}(G)$.
Diana R, Binu Selin T
doaj   +1 more source

Chromatic number and signless Laplacian spectral radius of graphs [PDF]

open access: yesTransactions on Combinatorics, 2022
For any simple graph $G$, the signless Laplacian matrix of $G$ is defined as $D(G)+A(G)$, where $D(G)$ and $A(G)$ are the diagonal matrix of vertex degrees and the adjacency matrix of $G$, respectively.
Mohammad Reza Oboudi
doaj   +1 more source

Sparse Graph Learning Under Laplacian-Related Constraints

open access: yesIEEE Access, 2021
We consider the problem of learning a sparse undirected graph underlying a given set of multivariate data. We focus on graph Laplacian-related constraints on the sparse precision matrix that encodes conditional dependence between the random variables ...
Jitendra K. Tugnait
doaj   +1 more source

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