Results 91 to 100 of about 121,870 (266)

Maximizing the smallest eigenvalue of grounded Laplacian matrix

Journal of Global Optimization
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, Run Wang, Wei Li, Zhongzhi Zhang   +3 more
openaire   +3 more sources

Numerical Investigation of a Diffusive SIR Model: Focus on Positivity Preservation

Mathematical Methods in the Applied Sciences, EarlyView.
ABSTRACT In this paper, we consider a system of semilinear partial differential equations (PDEs) representing a spatially extended SIR epidemic model. A brief analytical investigation of the well‐posedness and positivity of the solutions is provided in the appendix, while the main focus is on the numerical treatment of the model.
Rahele Mosleh, Claudia Panico, Messoud Efendiyev   +2 more
wiley   +1 more source

On distance signless Laplacian spectrum and energy of graphs

Electronic Journal of Graph Theory and Applications, 2018
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 ‎D‎Q(G) = Tr(G) + D(G)‎, ‎where D(G) is the distance matrix of G and Tr(G) is the diagonal ...
Abdollah Alhevaz, Maryam Baghipur, Ebrahim Hashemi   +2 more
doaj   +1 more source

On the spectral radius and energy of signless Laplacian matrix of digraphs. [PDF]

Heliyon, 2022
Ganie HA, Shang Y.
europepmc   +1 more source

Advancing Quantitative Susceptibility Mapping With 2.5D Diffusion Models for Rapid Intracranial Hemorrhage Quantification

Magnetic Resonance in Medicine, EarlyView.
ABSTRACT Purpose To develop a generative diffusion model‐based approach for robust and efficient quantitative susceptibility mapping (QSM) reconstruction in intracranial hemorrhage (ICH), applicable to both standard gradient echo (GRE) and rapid echo planar imaging (EPI) acquisitions.
Zhuang Xiong, Yang Gao, Feng Liu, Derek Emery, Ken Butcher, Alan H. Wilman, Hongfu Sun   +6 more
wiley   +1 more source

On the Signless Laplacian ABC-Spectral Properties of a Graph

Mathematics
In the paper, we introduce the signless Laplacian ABC-matrix Q̃(G)=D¯(G)+Ã(G), where D¯(G) is the diagonal matrix of ABC-degrees and Ã(G) is the ABC-matrix of G. The eigenvalues of the matrix Q̃(G) are the signless Laplacian ABC-eigenvalues of G.
Bilal A. Rather, Hilal A. Ganie, Yilun Shang   +2 more
doaj   +1 more source

On the Seidel Laplacian spectrum of threshold graphs [PDF]

Journal of Hyperstructures
A graph which does not contain C4, P4, or 2K2 as its induced subgraphs, is called a threshold graph. In this paper, we consider seidel laplacian matrix of a connected threshold graph and determine the seidel laplacian spectrum. Also, the characterization
Megha P M, Parvathy K S
doaj   +1 more source

Largest Eigenvalue of the Laplacian Matrix

, 2015
Following an editorial request, this is the second part of the article originally available in arxiv:1405.4880v1, corresponding to Section 6 of that manuscript. Several clarification comments and improvements to the original exposition were added, and the introduction and background materials are new. No new mathematical content was added.
openaire   +2 more sources

Robustness Assessment of Public Transport Networks in Various Graph Representations: Systematic Review, Decision Support, and Case Study

Networks, EarlyView.
ABSTRACT The analysis of certain properties of the underlying graph of a public transport network generates insights about the network's structure. Hereby, the choice of the graph representation depends on a trade‐off between complexity reduction and information preservation to adequately model a public transport network.
Michael Palk, Stefan Voß, Raka Jovanovic   +2 more
wiley   +1 more source

Robust joint clustering of multi-omics single-cell data via multi-modal high-order neighborhood Laplacian matrix optimization. [PDF]

Bioinformatics, 2023
Jiang H, Zhan S, Ching WK, Chen L.
europepmc   +1 more source