Results 101 to 110 of about 2,218,762 (336)
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
doaj +1 more source
Laplacian matrices of weighted digraphs represented as quantum states
, 2017 Representing graphs as quantum states is becoming an increasingly important approach to study entanglement of mixed states, alternate to the standard linear algebraic density matrix-based approach of study.
Adhikari, Bibhas +3 more
core +1 more source
Unimodular congruence of the Laplacian matrix of a graph
Linear Algebra and its Applications, 1994 AbstractLet G be a graph with vertices 1, 2, …, n. Associated with G, there is an integral quadratic form, Q(x), on the n-tuple of indeterminates x = (x1, …, xn), given by Q(x) = Σ(xi − xj)2, where the sum is taken over all edges (i, j) of G. In this paper we prove that the quadratic forms Q1, Q2 associated with graphs G1, G2 are congruent by a ...
openaire +2 more sources
Asian Journal of Control, EarlyView.Abstract This paper focuses on the issue of adaptive event‐triggered containment control for Markov jump multi‐agent systems characterized by hidden Markov jump parameters. The central objective is to design an output‐feedback controller for the Markov jump multi‐agent system by using an adaptive event‐triggered technique that not only ensures the ...
Parivallal Arumugam +3 more
wiley +1 more source
Two Laplacians for the distance matrix of a graph
Linear Algebra and its Applications, 2013 Abstract We introduce a Laplacian and a signless Laplacian for the distance matrix of a connected graph, called the distance Laplacian and distance signless Laplacian , respectively. We show the equivalence between the distance signless Laplacian, distance Laplacian and the distance spectra for the class of transmission regular graphs.
Pierre Hansen, Mustapha Aouchiche
openaire +2 more sources
On Seidel Laplacian matrix and energy of graphs
Acta Universitatis Sapientiae, Informatica, 2022 Abstract In this work, the Seidel Laplacian spectrum of graphs are determined. Then new bounds are presented for the Seidel Laplacian energy of regular graphs and graphs by using their Seidel Laplacian spectrum and other techniques. Further, the Seidel Laplacian energy of specific graphs are computed.
openaire +2 more sources
Reduction of CO2 and Related Compounds with a Low‐Valent CaI Synthon
ChemistryEurope, EarlyView.The reduction of CO2, CS2, or carbodiimide with a CaI synthon results in formation of calcium complexes with CO32−, CS22− or C–C coupled carbodiimide dianions. Differences between reduction of these reagents with the CaI synthon and established MgI complexes are discussed. Combined experimental and computational investigations give insight in the first
Stefan Thum +4 more
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
doaj +1 more source
Matrix tree theorem for the net Laplacian matrix of a signed graph
Linear and Multilinear Algebra, 2023 For a simple signed graph $G$ with the adjacency matrix $A$ and net degree matrix $D^{\pm}$, the net Laplacian matrix is $L^{\pm}=D^{\pm}-A$. We introduce a new oriented incidence matrix $N^{\pm}$ which can keep track of the sign as well as the orientation of each edge of $G$. Also $L^{\pm}=N^{\pm}(N^{\pm})^T$.
openaire +2 more sources
Efficient and model‐agnostic parameter estimation under privacy‐preserving post‐randomization data
Canadian Journal of Statistics, EarlyView.Abstract Balancing data privacy with public access is critical for sensitive datasets. However, even after de‐identification, the data are still vulnerable to, for example, inference attacks (by matching some keywords with external datasets). Statistical disclosure control (SDC) methods offer additional protection, and the post‐randomization method ...
Qinglong Tian, Jiwei Zhao
wiley +1 more source