Results 71 to 80 of about 76,384 (242)
What is a proper graph Laplacian? An operator-theoretic framework for graph diffusion
Special MatricesWe introduce an operator-theoretic definition of a proper graph Laplacian as any matrix associated with a given graph that can be expressed as the composition of a divergence and a gradient operator, with the gradient acting between graph-related spaces ...
Estrada Ernesto
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The First Zagreb Index, the Laplacian Spectral Radius, and Some Hamiltonian Properties of Graphs
MathematicsThe first Zagreb index of a graph G is defined as the sum of the squares of the degrees of all the vertices in G. The Laplacian spectral radius of a graph G is defined as the largest eigenvalue of the Laplacian matrix of the graph G.
Rao Li
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Spectra of Graphs Resulting from Various Graph Operations and Products: a Survey
Special Matrices, 2018 Let G be a graph on n vertices and A(G), L(G), and |L|(G) be the adjacency matrix, Laplacian matrix and signless Laplacian matrix of G, respectively. The paper is essentially a survey of known results about the spectra of the adjacency, Laplacian and ...
Barik S., Kalita D., Pati S., Sahoo G.
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Certain Energies of Graphs for Dutch Windmill and Double-Wheel Graphs
Journal of Mathematics, 2022 Energy of a graph is defined as the sum of the absolute values of the eigenvalues of the adjacency matrix associated with the graph. In this research work, we find color energy, distance energy, Laplacian energy, and Seidel energy for the Dutch windmill ...
Jing Wu +3 more
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Graph Laplacian for Semi-supervised Learning
, 2023 Semi-supervised learning is highly useful in common scenarios where labeled data is scarce but unlabeled data is abundant. The graph (or nonlocal) Laplacian is a fundamental smoothing operator for solving various learning tasks. For unsupervised clustering, a spectral embedding is often used, based on graph-Laplacian eigenvectors.
Streicher, Or, Gilboa, Guy
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Advanced Intelligent Systems, EarlyView.Feature from recent image foundation models (DINOv2) are useful for vision tasks (segmentation, object localization) with little or no human input. Once upsampled, they can be used for weakly supervised micrograph segmentation, achieving strong results when compared to classical features (blurs, edge detection) across a range of material systems.
Ronan Docherty +2 more
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Graph Laplacians and Stabilization of Vehicle Formations [PDF]
, 2001 Control of vehicle formations has emerged as a topic of significant interest to the controls community. In this paper, we merge tools from graph theory and control theory to derive stability criteria for formation stabilization.
Fax, J. Alexander, Murray, Richard M.
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MathematicsMany properties of the structure and dynamics of complex networks derive from the characteristics of the spectrum of the associated Laplacian matrix, specifically from the set of its eigenvalues. In this paper, we show that there exist graphs for which the ratio between the length of the spectrum (that is, the difference between the largest and ...
Akhter S., Frasca M., Estrada E.
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GraphNeuralCloth: A Graph‐Neural‐Network‐Based Framework for Non‐Skinning Cloth Simulation
Advanced Intelligent Systems, EarlyView.This study presents a cloth motion capture system and a point‐cloud‐to‐mesh processing method to support the prediction of real‐world fabric deformation. GraphNeuralCloth, a graph neural‐network (GNN)‐based framework is also proposed to estimate the cloth morphology change in real time.
Yingqi Li +9 more
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Selective Sequestration of Toxic NOx Gases by P‐Doped Graphene: A Density Functional Theory Study
Advanced Physics Research, EarlyView.P‐doped graphene (P‐grap) is explored as an NOx sensor through DFT simulations. The analysis of its geometry, binding energies, electronic properties, and atom‐in‐molecule characteristics demonstrates that P‐grap is a selective sensor for NOx among a mixture of various gases.
Anwar Ali +3 more
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