title: Eigenvalues of normalized Laplacian matrices of fractal trees and dendrimers: analytical results and applications.
abstract: The eigenvalues of the normalized Laplacian matrix of a network play an important role in its structural and dynamical aspects associated with the network. In this paper, we study the spectra and their applications of normalized Laplacian matrices of a family of fractal trees and dendrimers modeled by Cayley trees, both of which are built in an iterative way. For the fractal trees, we apply the spectral decimation approach to determine analytically all the eigenvalues and their corresponding multiplicities, with the eigenvalues provided by a recursive relation governing the eigenvalues of networks at two successive generations. For Cayley trees, we show that all their eigenvalues can be obtained by computing the roots of several small-degree polynomials defined recursively. By using the relation between normalized Laplacian spectra and eigentime identity, we derive the explicit solution to the eigentime identity for random walks on the two treelike networks, the leading scalings of which follow quite different behaviors. In addition, we corroborate the obtained eigenvalues and their degeneracies through the link between them and the number of spanning trees.
authors.affiliation: School of Computer Science, Fudan University, Shanghai 200433, China.
authors.forename: Alafate
authors.initials: A
authors.lastname: Julaiti
authors.forename: Bin
authors.initials: B
authors.lastname: Wu
authors.forename: Zhongzhi
authors.initials: Z
authors.lastname: Zhang