A concept related to the spectrum of a graph is that of energy. The energy E(G) of a graph G is equal to the sum of the absolute values of the eigenvalues of the adjacency matrix of G . The Laplacian energy of a graph G is equal to the sum of distances of the Laplacian eigenvalues of G and the average degree d(G) of G. In this paper we introduce the concept of Laplacian energy of fuzzy graphs. ...

The Wiener index W(G) of a connected graph G is defined as the sum of the distances between all unordered pairs of vertices of G. The eccentricity of a vertex v in G is the distance to a vertex farthest from v. In this paper we obtain the Wiener index of a graph in terms of eccentricities. Further we extend these results to the self-centered graphs.

a concept related to the spectrum of a graph is that of energy. the energy e(g) of a graph g is equal to the sum of the absolute values of the eigenvalues of the adjacency matrix of g . the laplacian energy of a graph g is equal to the sum of distances of the laplacian eigenvalues of g and the average degree d(g) of g. in this paper we introduce the concept of laplacian energy of fuzzy graphs. ...

The eccentricity matrix ε(G) of a graph G is constructed from the distance by keeping only largest distances for each row and column. This can be interpreted as opposite adjacency obtained equal to 1 ε-eigenvalues are those its ε(G). Wang et al. [24] proposed problem determining maximum ε-spectral radius trees with given order. In this paper, we consider above n-vertex diameter. fixed odd diame...

The eccentricity of a vertex is the maximum distance from it to another vertex and the average eccentricity ecc(G) of a graph G is the mean value of eccentricities of all vertices of G. The average eccentricity is deeply connected with a topological descriptor called the eccentric connectivity index, defined as a sum of products of vertex degrees and eccentricities. In this paper we analyze ext...

We attach a certain n × n matrix An to the Dirichlet series L(s) = ∑ ∞ k=1 akk . We study the determinant, characteristic polynomial, eigenvalues, and eigenvectors of these matrices. The determinant of An can be understood as a weighted sum of the first n coefficients of the Dirichlet series L(s). We give an interpretation of the partial sum of a Dirichlet series as a product of eigenvalues. In...

The eccentricity matrix E(G) of a connected graph G is obtained from the distance by keeping largest nonzero entries in each row and column, leaving zeros remaining ones. eigenvalues are E-eigenvalues G. In this article, we find inertia matrices trees. Interestingly, any tree on more than 4 vertices with odd diameter has exactly two positive negative (irrespective structure tree). Also, show th...

We will discuss a few basic facts about the distribution of eigenvalues of the adjacency matrix, and some applications. Then we discuss the question of computing the eigenvalues of a symmetric matrix. 1 Eigenvalue distribution Let us consider a d-regular graph G on n vertices. Its adjacency matrix AG is an n× n symmetric matrix, with all of its eigenvalues lying in [−d, d]. How are the eigenval...