Duplication-divergence growing graph models

2025/06/18 by Dario Borrelli, Borrelli, Dario
Computer Science · #Adaptation and Self-Organizing Systems (nlin.AO) #Advanced Database Systems and Queries #Data Management and Algorithms #FOS: Biological sciences #FOS: Physical sciences #Graph Theory and Algorithms #Molecular Networks (q-bio.MN) #Physics and Society (physics.soc-ph) #Statistical Mechanics (cond-mat.stat-mech)

paper · pdf · doi:10.48550/arxiv.2506.15640

openalex publication_date 2025/06/18 · openalex created_date 2025/10/19 · openalex updated_date 2026/07/28

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.

Citations

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Related

• Divergence asymmetry and connected components in a general duplication-divergence graph model
• On the concentration of the maximum degree in the duplication-divergence models
• The expected degree distribution in transient duplication divergence models
• Isolated vertices in two duplication-divergence models with edge deletion
• Duplication Models for Biological Networks