scholarly journals Degree Subtraction Adjacency Polynomial and Energy of Graphs obtained from Complete Graph

2020 ◽  
pp. 263-277
Author(s):  
Harishchandra S. Ramane ◽  
Hemaraddi N. Maraddi ◽  
Daneshwari Patil ◽  
Kavita Bhajantri
Keyword(s):  
Characteristic Polynomial ◽  
Complete Graph ◽  
Adjacency Matrix ◽  
Degree Of A Vertex ◽  
The Absolute ◽  
Square Matrix

The degree subtraction adjacency matrix of a graph G is a square matrix DSA(G)=[dij], in which dij=d(vi)-d(vj), if the vertices vi and vj are adjacent and dij=0, otherwise, where d(u) is the degree of a vertex u. The DSA energy of a graph is the sum of the absolute values of the eigenvalues of DSA matrix. In this paper, we obtain the characteristic polynomial of the DSA matrix of graphs obtained from the complete graph. Further we study the DSA energy of these graphs.

scholarly journals Seidel Equienergetic Graphs

2016 ◽  
Vol 16 ◽  
pp. 62-69
Author(s):  
Harishchandra S. Ramane ◽  
Mahadevappa M. Gundloor ◽  
Sunilkumar M. Hosamani
Keyword(s):  
Characteristic Polynomial ◽  
Regular Graphs ◽  
Seidel Matrix ◽  
The Absolute ◽  
Equienergetic Graphs ◽  
Square Matrix

The Seidel matrix S(G) of a graph G is the square matrix with diagonal entries zeroes and off diagonal entries are – 1 or 1 corresponding to the adjacency and non-adjacency. The Seidel energy SE(G) of G is defined as the sum of the absolute values of the eigenvalues of S(G). Two graphs G1 and G2 are said to be Seidel equienergetic if SE(G1) = SE(G2). We establish an expression for the characteristic polynomial of the Seidel matrix and for the Seidel energy of the join of regular graphs. Thereby construct Seidel non cospectral, Seidel equienergetic graphs on n vertices, for all n ≥ 12

scholarly journals The normalized distance Laplacian

2021 ◽  
Vol 9 (1) ◽  
pp. 1-18
Author(s):  
Carolyn Reinhart
Keyword(s):  
Spectral Radius ◽  
Characteristic Polynomial ◽  
Adjacency Matrix ◽  
Connected Graph ◽  
Distance Matrix ◽  
Laplacian Matrix ◽  
Diagonal Matrix ◽  
The Matrix

Abstract The distance matrix 𝒟(G) of a connected graph G is the matrix containing the pairwise distances between vertices. The transmission of a vertex vi in G is the sum of the distances from vi to all other vertices and T(G) is the diagonal matrix of transmissions of the vertices of the graph. The normalized distance Laplacian, 𝒟𝒧(G) = I−T(G)−1/2 𝒟(G)T(G)−1/2, is introduced. This is analogous to the normalized Laplacian matrix, 𝒧(G) = I − D(G)−1/2 A(G)D(G)−1/2, where D(G) is the diagonal matrix of degrees of the vertices of the graph and A(G) is the adjacency matrix. Bounds on the spectral radius of 𝒟 𝒧 and connections with the normalized Laplacian matrix are presented. Twin vertices are used to determine eigenvalues of the normalized distance Laplacian. The distance generalized characteristic polynomial is defined and its properties established. Finally, 𝒟𝒧-cospectrality and lack thereof are determined for all graphs on 10 and fewer vertices, providing evidence that the normalized distance Laplacian has fewer cospectral pairs than other matrices.

Binomial incidence matrix of a semigraph

2020 ◽  
pp. 2150017
Author(s):  
Jyoti Shetty ◽  
G. Sudhakara
Keyword(s):  
Graph Theory ◽  
Complete Graph ◽  
Adjacency Matrix ◽  
Incidence Matrix ◽  
Systematic Approach ◽  
Line Graph ◽  
The Matrix ◽  
Theory Of Graphs

A semigraph, defined as a generalization of graph by  Sampathkumar, allows an edge to have more than two vertices. The idea of multiple vertices on edges gives rise to multiplicity in every concept in the theory of graphs when generalized to semigraphs. In this paper, we define a representing matrix of a semigraph [Formula: see text] and call it binomial incidence matrix of the semigraph [Formula: see text]. This matrix, which becomes the well-known incidence matrix when the semigraph is a graph, represents the semigraph uniquely, up to isomorphism. We characterize this matrix and derive some results on the rank of the matrix. We also show that a matrix derived from the binomial incidence matrix satisfies a result in graph theory which relates incidence matrix of a graph and adjacency matrix of its line graph. We extend the concept of “twin vertices” in the theory of graphs to semigraph theory, and characterize them. Finally, we derive a systematic approach to show that the binomial incidence matrix of any semigraph on [Formula: see text] vertices can be obtained from the incidence matrix of the complete graph [Formula: see text].

scholarly journals Some New Results on Various Graph Energies of the Splitting Graph

2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Zheng-Qing Chu ◽  
Saima Nazeer ◽  
Tariq Javed Zia ◽  
Imran Ahmed ◽  
Sana Shahid
Keyword(s):  
Adjacency Matrix ◽  
Connected Graph ◽  
Absolute Value ◽  
Regular Splitting ◽  
The Absolute ◽  
Splitting Graph ◽  
Simple Connected Graph

The energy of a simple connected graph G is equal to the sum of the absolute value of eigenvalues of the graph G where the eigenvalue of a graph G is the eigenvalue of its adjacency matrix AG. Ultimately, scores of various graph energies have been originated. It has been shown in this paper that the different graph energies of the regular splitting graph S′G is a multiple of corresponding energy of a given graph G.

On the energy and Estrada index of Cayley graphs

2015 ◽  
Vol 07 (01) ◽  
pp. 1550005 ◽  
Author(s):  
Modjtaba Ghorbani
Keyword(s):  
Adjacency Matrix ◽  
Cayley Graphs ◽  
Estrada Index ◽  
The Absolute

The concept of energy of a graph was first defined in 1978 by Gutman as the sum of the absolute values of the eigenvalues of its adjacency matrix. Let λ1, λ2, …, λn be eigenvalues of graph Γ, then the Estrada index of Γ is defined as [Formula: see text]. The aim of this paper is to estimate the energy and Estrada index of Cayley graphs Cay (G, S) where G ≅ D2n, U6n and S is a normal symmetric generating subset of G.

scholarly journals On the Seidel Energy of Certain Mesh Derived Networks

2019 ◽  
Vol 9 (2) ◽  
pp. 1905-1910
Keyword(s):  
Adjacency Matrix ◽  
Seidel Matrix ◽  
Energy Of Graph ◽  
The Absolute ◽  
Manual Calculation

The energy of graph G is defined as the sum of the absolute values of eigenvalues of the adjacency matrix A(G). The manual calculation of energy of graphs consumes several man hours. In this paper, we use MATLAB to generate the Seidel matrix and hence calculate the Seidel energy of some mesh derived networks.

scholarly journals A Note on Skew Energy of Hadamard Graph

2019 ◽  
Vol 9 (1S4) ◽  
pp. 1010-1013
Keyword(s):  
Adjacency Matrix ◽  
Oriented Graph ◽  
Eigen Values ◽  
Skew Energy ◽  
The Absolute

The skew spectrum and skew energy of an oriented graph are respectively the set of eigenvalues of the adjacency matrix of and the sum of the absolute values of the eigen values of the adjacency matrix of . In this work, we find and study the skew spectrum and the skew energy of Hadamard graph for a particular orientation.

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