Results 71 to 80 of about 1,705,971 (292)
Moment-Based Spectral Analysis of Large-Scale Generalized Random Graphs
IEEE Access, 2017 This paper investigates the spectra of the adjacency matrix and Laplacian matrix for an artificial complex network model-the generalized random graph. We deduce explicit expressions for the first four asymptotic spectral moments of the adjacency matrix ...
Qun Liu, Zhishan Dong, En Wang
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Maximizing the smallest eigenvalue of grounded Laplacian matrix
Journal of Global OptimizationFor 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
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Laplacian versus adjacency matrix in quantum walk search [PDF]
Quantum Information Processing, 2016 A quantum particle evolving by Schrödinger's equation contains, from the kinetic energy of the particle, a term in its Hamiltonian proportional to Laplace's operator. In discrete space, this is replaced by the discrete or graph Laplacian, which gives rise to a continuous-time quantum walk.
Thomas G. Wong +2 more
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Bipartite subgraphs and the signless Laplacian matrix [PDF]
, 2011 For a connected graph G, we derive tight inequalities relating the smallest signless Laplacian eigenvalue to the largest normalized Laplacian eigenvalue.
Debdas Paul, Steve Kirkland
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Group inverse matrix of the normalized Laplacian on subdivision networks [PDF]
, 2020 In this paper we consider a subdivision of a given network and we show how the group inverse matrix of the normalized laplacian of the subdivision network is related to the group inverse matrix of the normalized laplacian of the initial given network ...
Mitjana Riera, Margarida +2 more
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Advanced Intelligent Discovery, EarlyView.Machine learning serves as a central engine for the intelligent characterization of two‐dimensional materials by integrating multimodal techniques, including optical microscopy, spectroscopy, electron microscopy, and scanning probe microscopy (SPM). This unified framework enables automated, high‐throughput, and quantitative extraction of structural ...
Zhi‐Long Cao, Jia‐Xu Yan
wiley +1 more source
Permanent of the laplacian matrix of trees with a given matching
Discrete Mathematics, 1986 Let \(G=(V,E)\) be an arbitrary graph, and \(d_ i>0\) be the degree of the vertex \(v_ i\in V\). D(G) denotes the diagonal matrix whose (i,i)-entry is \(d_ i\), and A(G) denotes the adjacency matrix of the graph G. The matrix \(L(G)=D(G)-A(G)\) will be called the Laplacian matrix of the graph G.
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Advanced Intelligent Systems, EarlyView.A scalable distributed observer framework for LiDAR sensor networks is presented for robust tracking and pose estimation of mobile robots. By combining adaptable 3D detection, clustering‐based communication reduction, and intermittent inertial fusion, accurate and efficient estimation is achieved under occlusions and sensing constraints, with formally ...
Isabella Luppi, Ehsan Hashemi
wiley +1 more source
On the Eigenvalues and Energy of the Seidel and Seidel Laplacian Matrices of Graphs
Discrete Dynamics in Nature and SocietyLet SΓ be a Seidel matrix of a graph Γ of order n and let DΓ=diagn−1−2d1,n−1−2d2,…,n−1−2dn be a diagonal matrix with di denoting the degree of a vertex vi in Γ. The Seidel Laplacian matrix of Γ is defined as SLΓ=DΓ−SΓ.
J. Askari +2 more
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Laplacian matrix learning for smooth graph signal representation [PDF]
2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2015 The construction of a meaningful graph plays a crucial role in the emerging field of signal processing on graphs. In this paper, we address the problem of learning graph Laplacians, which is similar to learning graph topologies, such that the input data form graph signals with smooth variations on the resulting topology.
Dong, Xiaowen +3 more
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