Entropy of the Gibbs state cannot distinguish complex graph models
Year: 2020
Type: preprint
Abstract: In this work we study the entropy of the Gibbs state corresponding to a graph. The Gibbs state is obtained from the Laplacian, normalized Laplacian or adjacency matrices associated with a graph. We show that for a large number of graph models this approach does not distinguish the models asymptotically. We illustrate our analytical results with numerical simulations for Erdős-Renyi, Watts-Strogatz, Barabasi-Albert and Chung-Lu graph models. We conclude saying that, from this perspective, these models are boring.
Source: arXiv (Cornell University)
Authors: Adam Glos, Aleksandra Krawiec, Łukasz Pawela
Language: English
Subfield: Discrete Mathematics and Combinatorics
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3011007922