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1. Verfasser: Borrelli, Dario
Format: Preprint
Veröffentlicht: 2025
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Online-Zugang:https://arxiv.org/abs/2506.15640
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author Borrelli, Dario
author_facet Borrelli, Dario
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2506_15640
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Duplication-divergence growing graph models
Borrelli, Dario
Statistical Mechanics
Adaptation and Self-Organizing Systems
Physics and Society
Molecular Networks
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.
title Duplication-divergence growing graph models
topic Statistical Mechanics
Adaptation and Self-Organizing Systems
Physics and Society
Molecular Networks
url https://arxiv.org/abs/2506.15640