Counting spanning trees in self-similar networks by evaluating determinants

TL;DR

This paper introduces a novel analytical method for counting spanning trees in self-similar networks by evaluating determinants of Laplacian submatrices, avoiding computational complexity in large networks.

Contribution

The authors develop a generic technique to compute the number of spanning trees in self-similar networks, demonstrated on $(x,y)$-flowers and other models, linking network topology to spanning tree enumeration.

Findings

Exact number of spanning trees derived analytically for $(x,y)$-flowers.

Method applicable to various self-similar networks with different degree distributions.

Topological features like degree distribution influence the number of spanning trees.

Abstract

Spanning trees are relevant to various aspects of networks. Generally, the number of spanning trees in a network can be obtained by computing a related determinant of the Laplacian matrix of the network. However, for a large generic network, evaluating the relevant determinant is computationally intractable. In this paper, we develop a fairly generic technique for computing determinants corresponding to self-similar networks, thereby providing a method to determine the numbers of spanning trees in networks exhibiting self-similarity. We describe the computation process with a family of networks, called $(x,y)$-flowers, which display rich behavior as observed in a large variety of real systems. The enumeration of spanning trees is based on the relationship between the determinants of submatrices of the Laplacian matrix corresponding to the $(x,y)$-flowers at different generations and is devoid of the direct laborious computation of determinants. Using the proposed method, we derive analytically the exact number of spanning trees in the $(x,y)$-flowers, on the basis of which we also obtain the entropies of the spanning trees in these networks. Moreover, to illustrate the universality of our technique, we apply it to some other self-similar networks with distinct degree distributions, and obtain explicit solutions to the numbers of spanning trees and their entropies. Finally, we compare our results for networks with the same average degree but different structural properties, such as degree distribution and fractal dimension, and uncover the effect of these topological features on the number of spanning trees.