Results 21 to 30 of about 316,679 (279)
Intersection dimension and graph invariants
Discussiones Mathematicae Graph Theory, 2021 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Aravind N.R., Subramanian C.R.
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Scale-invariant geometric random graphs [PDF]
Physical Review E, 2016 7 pages, 8 ...
Xie, Zheng, Rogers, Tim
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The Foliage Partition: An Easy-to-Compute LC-Invariant for Graph States [PDF]
QuantumThis paper introduces the foliage partition, an easy-to-compute LC-invariant for graph states, of computational complexity $\mathcal{O}(n^3)$ in the number of qubits.
Adam Burchardt, Frederik Hahn
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Eccentric topological properties of a graph associated to a finite dimensional vector space
Main Group Metal Chemistry, 2020 A topological index is actually designed by transforming a chemical structure into a number. Topological index is a graph invariant which characterizes the topology of the graph and remains invariant under graph automorphism.
Liu Jia-Bao +5 more
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On the r-dynamic coloring of some fan graph families
Analele Stiintifice ale Universitatii Ovidius Constanta: Seria Matematica, 2021 In this paper, we determine the r-dynamic chromatic number of the fan graph Fm,n and determine sharp bounds of this graph invariant for four related families of graphs: The middle graph M(Fm,n), the total graph T (Fm,n), the central graph C(Fm,n) and the
Falcón Raúl M. +3 more
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Representation of an invariant measure of irreducible discrete-time Markov chain with a finite state space by a set of opposite directed trees [PDF]
Компьютерные исследования и моделирование, 2015 A problem of finding of an invariant measure of irreducible discrete-time Markov chain with a finite state space is considered. There is a unique invariant measure for such Markov chain that can be multiplied by an arbitrary constant. A representation of
Alexej Lvovich Krugly
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Convolutional Neural Network Outperforms Graph Neural Network on the Spatially Variant Graph Data
Mathematics, 2023 Applying machine learning algorithms to graph-structured data has garnered significant attention in recent years due to the prevalence of inherent graph structures in real-life datasets.
Anna Boronina +2 more
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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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Group-invariant Percolation on Graphs [PDF]
Geometric And Functional Analysis, 1999 For a closed group \(G\) of automorphisms of a graph \(X\), geometric properties such as amenability or unimodularity are related to \(G\)-invariant percolation processes. Origins of the problem may be situated in Kesten's theorem [\textit{H. Kesten}, Trans. Am. Math. Soc.
Benjamini, I. +3 more
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On invariant sets in Lagrangian graphs [PDF]
, 2010 In this exposition, we show that a Hamiltonian is always constant on a compact invariant connected subset which lies in a Lagrangian graph provided that the Hamiltonian and the graph are smooth enough.
A. Fathi +9 more
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