Expectation and Variance of the Degree of a Node in Random Spanning Trees

TL;DR

This paper derives analytical formulas for the expectation, variance, and covariance of node degrees in random spanning trees under a Gibbs distribution, using the Matrix Tree Theorem and extending to directed graphs.

Contribution

It introduces a novel analytical framework for computing degree moments in weighted spanning trees, distinguishing between probability and degree weights, and extends results to directed graphs.

Findings

Derived explicit formulas for degree expectation and variance.

Showed dependence on the inverse of a submatrix of the graph Laplacian.

Extended results from undirected to directed graphs.

Abstract

We consider a Gibbs distribution over all spanning trees of an undirected, edge weighted finite graph, where, up to normalization, the probability of each tree is given by the product of its edge weights. Defining the weighted degree of a node as the sum of the weights of its incident edges, we present analytical expressions for the expectation, variance and covariance of the weighted degree of a node across the Gibbs distribution. To generalize our approach, we distinguish between two types of weight: probability weights, which regulate the distribution of spanning trees, and degree weights, which define the weighted degree of nodes. This distinction allows us to define the weighted degree of nodes independently of the probability weights. By leveraging the Matrix Tree Theorem, we show that these degree moments ultimately depend on the inverse of a submatrix of the graph Laplacian. While our focus is on undirected graphs, we demonstrate that our results can be extended to the directed setting by considering incoming directed trees instead.