Duplication-divergence growing graph models
Abstract
In recent decades, it has been emphasized that the evolving structure of networks may be shaped by interaction principles that yield sparse graphs with a vertex degree distribution exhibiting an algebraic tail, and other structural traits that are not featured in traditional random graphs. In this respect, through a mean-field approach, this review tackles the statistical physics of graph models based on the interaction principle of duplication-divergence. Additional sophistications extending the duplication-divergence model are also reviewed as well as generalizations of other known models. Possible research gaps and related prior results are then discussed.
Cite
@article{arxiv.2506.15640,
title = {Duplication-divergence growing graph models},
author = {Dario Borrelli},
journal= {arXiv preprint arXiv:2506.15640},
year = {2025}
}
Comments
45 pages, 5 figures, 1 table, review article (v2), some edits and rephrasing in main text and figures caption