Results 11 to 20 of about 18,314 (299)

The gamma-Signless Laplacian Adjacency Matrix of Mixed Graphs [PDF]

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   +3 more sources

Approximations of the Generalized Inverse of the Graph Laplacian Matrix [PDF]

open access: yesInternet Mathematics, 2012
We devise methods for finding approximations of the generalized inverse of the graph Laplacian matrix, which arises in many graph-theoretic applications. Finding this matrix in its entirety involves solving a matrix inversion problem, which is resource-demanding in terms of consumed time and memory and hence impractical whenever the graph is relatively
BOZZO, Enrico, FRANCESCHET, Massimo
openaire   +5 more sources

Interlacing Properties of Eigenvalues of Laplacian and Net-Laplacian Matrix of Signed Graphs [PDF]

open access: yes, 2023
This paper explores interlacing inequalities in the Laplacian spectrum of signed cycles and investigates interlacing relationship between the spectrum of the net-Laplacian of a signed graph and its subgraph formed by removing a vertex together with its incident edges.
Guragain, Satyam, Srivastava, Ravi
openaire   +3 more sources

A note on a conjecture for the distance Laplacian matrix [PDF]

open access: yesThe Electronic Journal of Linear Algebra, 2016
In this note, the graphs of order n having the largest distance Laplacian eigenvalue of multiplicity n −2 are characterized. In particular, it is shown that if the largest eigenvalue of the distance Laplacian matrix of a connected graph G of order n has multiplicity n − 2, then G = S_n or G = K_(p,p), where n = 2p.
Marques da Silva, Celso jun.   +2 more
openaire   +3 more sources

Maximizing the smallest eigenvalue of grounded Laplacian matrix [PDF]

open access: yesJournal of Global Optimization, 2023
For a connected graph $\mathcal{G}=(V,E)$ with $n$ nodes, $m$ edges, and Laplacian matrix $\boldsymbol{\mathit{L}}$, a grounded Laplacian matrix $\boldsymbol{\mathit{L}}(S)$ of $\mathcal{G}$ is a $(n-k) \times (n-k)$ principal submatrix of $\boldsymbol{\mathit{L}}$, obtained from $\boldsymbol{\mathit{L}}$ by deleting $k$ rows and columns corresponding ...
Xiaotian Zhou   +3 more
openaire   +4 more sources

Pseudoinverses of Signed Laplacian Matrices [PDF]

open access: yes, 2023
Even for nonnegative graphs, the pseudoinverse of a Laplacian matrix is not an ``ordinary (i.e., unsigned) Laplacian matrix but rather a signed Laplacian.
Fontan, Angela,   +5 more
core   +1 more source

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

NEW BOUNDS AND EXTREMAL GRAPHS FOR DISTANCE SIGNLESS LAPLACIAN SPECTRAL RADIUS [PDF]

open access: yesJournal of Algebraic Systems, 2021
The distance signless Laplacian spectral radius of a connected graph $G$ is the largest eigenvalue of the distance signless Laplacian matrix of $G$, defined as $D^{Q}(G)=Tr(G)+D(G)$, where $D(G)$ is the distance matrix of $G$ and $Tr(G)$ is the diagonal ...
A. Alhevaz, M. Baghipur, S. Paul
doaj   +1 more source

On maximum degree (signless) Laplacian matrix of a graph [PDF]

open access: yes, 2022
Let G be a simple graph on n vertices and v1, v2, . . . , vn be the vertices ofG. We denote the degree of a vertex vi in G by dG(vi) = di. The maximumdegree matrix of G, denoted by M(G), is the real symmetric matrix withits ijth entry equal to max{di, dj}
Raghu, V. D.   +2 more
core   +1 more source

Laplacian Matrix for Dimensionality Reduction and Clustering [PDF]

open access: yes, 2020
Many problems in machine learning can be expressed by means of a graph with nodes representing training samples and edges representing the relationship between samples in terms of similarity, temporal proximity, or label information. Graphs can in turn be represented by matrices. A special example is the Laplacian matrix, which allows us to assign each
Laurenz Wiskott, Fabian Schönfeld
openaire   +2 more sources

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