Correlation between residual entropy and spanning tree entropy of ice-type models on graphs

The logarithm of the number of Eulerian orientations, normalised by the number of vertices, is known as the residual entropy in studies of ice-type models on graphs. The spanning tree entropy depends similarly on the number of spanning trees. We demonstrate and investigate a remarkably strong, though non-deterministic, correlation between these two entropies. This leads us to propose a new heuristic estimate for the residual entropy of regular graphs that performs much better than previous heuristics. We also study the expansion properties and residual entropy of random graphs with given degrees.

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Publication Details

Journal
Annales de l’Institut Henri Poincaré D Combinatorics Physics and their Interactions
Published
2025-03-25
DOI
https://doi.org/10.4171/aihpd/207
Primary Topic
Complex Network Analysis Techniques
Type
article
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article

Correlation between residual entropy and spanning tree entropy of ice-type models on graphs

Brendan D. McKay, Rui-Ray Zhang, Mikhail Isaev
1 links
Annales de l’Institut Henri Poincaré D Combinatorics Physics and their Interactions
Complex Network Analysis Techniques
article

Correlation between residual entropy and spanning tree entropy of ice-type models on graphs

Brendan D. McKay, Rui-Ray Zhang, Mikhail Isaev
article en

Abstract

The logarithm of the number of Eulerian orientations, normalised by the number of vertices, is known as the residual entropy in studies of ice-type models on graphs. The spanning tree entropy depends similarly on the number of spanning trees. We demonstrate and investigate a remarkably strong, though non-deterministic, correlation between these two entropies. This leads us to propose a new heuristic estimate for the residual entropy of regular graphs that performs much better than previous heuristics. We also study the expansion properties and residual entropy of random graphs with given degrees.

Annales de l’Institut Henri Poincaré D Combinatorics Physics and their Interactions
Australian National University (AU), UNSW Sydney (AU), Mathematical Sciences Research Institute (US)
Openalex Percentile: Top 95%
Complex Network Analysis Techniques
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