Our goal is to use the properties of the adjacency/Laplacian matrix of graphs to first understand the structure of the graph and, based on these insights, to design efficient algorithms. The study of algebraic properties of graphs is called algebraic graph theory. One of the most useful algebraic properties of graphs are the eigenvalues (and eigenvectors) of the adjacency/Laplacian matrix.

• Capital letters represent matrices and bold lower-case letters represent vectors. For a matrix A, aij denotes the element in row i and column j; for the vector x, xi denotes the i th entry in the vector. • Various special matrices are represented by the following conventions: The adjacency matrix is denoted A; the degree matrix is denoted D; the Laplacian D − A is denoted L. The Laplacian can...

Let G = (V,E) be a graph with vertex set V = {1, . . . , n} and edge set E. Throughout the paper, a graph G is undirected and simple (i.e., has no multi-edges or loops). We allow G to be disconnected. The Laplacian of G is the matrix L(G) = D−A, where D is the diagonal matrix whose entries are the degree of the vertices and A is the adjacency matrix of G. Chung’s normalized Laplacian L̃(G) [6] i...

Let Gl be the graph obtained from Kl by adhering the root of isomorphic trees T to every vertex of Kl, and dk−j+1 be the degree of vertices in the level j. In this paper we study the spectrum of the adjacency matrix A(Gl) and the Laplacian matrix L(Gl) for all positive integer l, and give some results about the spectrum of the adjacency matrix A(Gl) and the Laplacian matrix L(Gl). By using thes...

Again, this can be viewed as a change of variables; on the left-hand side, there are n diagonal free parameters wjj and n(n−1) 2 off-diagonal free parameters wjk, j < k in the matrix W (the remaining offdiagonal parameters are fixed, as W is symmetric); on the right-hand side, there are n free parameters in the matrix Λ and (n − 1) + (n − 2) + . . . + 1 + 0 = n(n−1) 2 free parameters in the mat...

A new distance measure between probability density functions (pdfs) is introduced, which we refer to as the Laplacian pdf distance. The Laplacian pdf distance exhibits a remarkable connection to Mercer kernel based learning theory via the Parzen window technique for density estimation. In a kernel feature space defined by the eigenspectrum of the Laplacian data matrix, this pdf distance is show...

Let G be a weighted graph with Laplacian matrix L and signless Laplacian matrix Q. In this note, block representations for the group inverse of L and Q are given. The resistance distance in a graph can be obtained from the block representation of the group inverse of L.

Recall that, given a graph G, the matrix Q = D + A is called the signless Laplacian, where A is the adjacency matrix and D is the diagonal matrix of vertex degrees. The matrix L = D − A is known as the Laplacian of G. Graphs with the same spectrum of an associated matrix M are called cospectral graphs with respect to M , or M–cospectral graphs. A graph H cospectral with a graph G, but not isomo...

We consider the p–Laplacian operator on a domain equipped with a Finsler metric. After deriving and recalling relevant properties of its first eigenfunction for p > 1, we investigate the limit problem as p → 1.

Given a C-function f on a compact riemannian manifold (X, g) we give a set of frequencies L = Lf (ε) depending on a small parameter ε > 0 such that the relative L-error ‖f−f ‖ ‖f‖ is bounded above by ε, where f L denotes the L-partial sum of the Fourier series f with respect to an orthonormal basis of L(X) constituted by eigenfunctions of the Laplacian operator ∆ associated to the metric g.