Broadcasting colourings on trees. A combinatorial view

TL;DR

This paper investigates the reconstruction problem in broadcasting colourings on trees, providing a combinatorial coupling method that works for smaller numbers of colours than previously known, with implications for sampling colourings in sparse graphs.

Contribution

It introduces a new combinatorial coupling approach for broadcasting colourings on trees that functions for k > 3d/ln d, improving understanding of the reconstruction threshold.

Findings

Coupling reduces disagreements at leaves for k > 3d/ln d

Results relate to sampling colourings in sparse random graphs

Provides a local decision-based coupling method

Abstract

The broadcasting models on a d-ary tree T arise in many contexts such as biology, information theory, statistical physics and computer science. We consider the k-colouring model, i.e. the root of T is assigned an arbitrary colour and, conditional on this assignment, we take a random colouring of T. A basic question here is whether the information of the assignment at the root affects the distribution of the colourings at the leaves. This is the so-called reconstruction/non-reconstruction problem. It is well known that d/ln d is a threshold function for this problem, i.e. * if k \geq (1+\eps)d/ln d, then the colouring of the root has a vanishing effect on the distribution of the colourings at the leaves, as the height of the tree grows * if $k\leq (1-\eps)d/ln d, then the colouring of the root biases the distribution of the colouring of the leaves regardless of the height of the tree. There is no apparent combinatorial reason why such a result should be true. When k\geq (1+\eps)d/ ln d, the threshold implies the following: We can couple two broadcasting processes that assign the root different colours such that the probability of having disagreement at the leaves reduces with their distance from the root. It is natural to perceive such coupling as a mapping from the colouring of the first broadcasting process to the colouring of the second one. Here, we study how can we have such a mapping "combinatorially". Devising a mapping where the disagreements vanish as we move away from the root turns out to be a non-trivial task to accomplish for any k \leq d. In this work we obtain a coupling which has the aforementioned property for any k>3d/ln d, i.e. much smaller than d. Interestingly enough, the decisions that we make in the coupling are local. We relate our result to sampling k-colourings of sparse random graphs, with expected degree d and k<d.