Results 11 to 20 of about 13,640 (282)
On the Laplacian, signless Laplacian and normalized Laplacian characteristic polynomials of a graph [PDF]
Czechoslovak Mathematical Journal, 2013 The authors prove a number of formulas on the characteristic polynomials of the Laplacian, signless Laplacian and normalized Laplacian matrices of graphs. The use of these formulas is exemplified in constructions of graphs cospectral with respect to the appropriate matrix.
Guo, Ji-Ming, Li, Jianxi, Shiu, Wai Chee
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Sparse Graph Learning Under Laplacian-Related Constraints
IEEE 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
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The G-Invariant Graph Laplacian
, 2023 Graph Laplacian based algorithms for data lying on a manifold have been proven effective for tasks such as dimensionality reduction, clustering, and denoising. In this work, we consider data sets whose data points lie on a manifold that is closed under the action of a known unitary matrix Lie group G.
Rosen, Eitan +4 more
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The Laplacian spectrum of a graph
Computers and Mathematics With Applications, 2004 Let G = (V, E) be a simple graph. Denote by D(G) the diagonal matrix of its vertexdegrees and by A(G) its adjacency matrix. Then, the Laplacian matrix of G is L(G) = D(G) − A(G).
Das, K.Ch.
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COUPLE GRAPH BASED LABEL PROPAGATION METHOD FOR HYPERSPECTRAL REMOTE SENSING DATA CLASSIFICATION [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2018 Graph based semi-supervised classification method are widely used for hyperspectral image classification. We present a couple graph based label propagation method, which contains both the adjacency graph and the similar graph. We propose to construct the
X. P. Wang, Y. Hu, J. Chen
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Graph Laplacians and Least Squares on Graphs [PDF]
2015 IEEE International Parallel and Distributed Processing Symposium Workshop, 2015 There are several classes of operators on graphs to consider in deciding on a collection of building blocks for graph algorithms. One class involves traditional graph operations such as breadth first or depth first search, finding connected components, spanning trees, cliques and other sub graphs, operations for editing graphs and so on.
Anil N. Hirani +2 more
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Acta Scientiarum: Technology, 2023 Spectral Graph theory has been utilized frequently in the field of Computer Vision and Pattern Recognition to address challenges in the field of Image Segmentation and Image Classification.
Subramaniam Usha +3 more
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Mathematics, 2021 Unravelling how the human brain structure gives rise to function is a central question in neuroscience and remains partially answered. Recent studies show that the graph Laplacian of the human brain’s structural connectivity (SC) plays a dominant role in
Jichao Ma +3 more
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The Laplacian Spread of Tricyclic Graphs [PDF]
The Electronic Journal of Combinatorics, 2009 The Laplacian spread of a graph is defined to be the difference between the largest eigenvalue and the second smallest eigenvalue of the Laplacian matrix of the graph. In this paper, we investigate Laplacian spread of graphs, and prove that there exist exactly five types of tricyclic graphs with maximum Laplacian spread among all tricyclic graphs of ...
Yanqing Chen, Ligong Wang 0001
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On distance Laplacian energy in terms of graph invariants
, 2023 summary:For a simple connected graph $G$ of order $n$ having distance Laplacian eigenvalues $ \rho ^{L}_{1}\geq \rho ^{L}_{2}\geq \cdots \geq \rho ^{L}_{n}$, the distance Laplacian energy ${\rm DLE} (G)$ is defined as ${\rm DLE} (G)=\sum _{i=1}^{n}|\rho ^
Rather, Bilal A. +3 more
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