Duplication-divergence growing graph models

TL;DR

This paper reviews duplication-divergence graph models, highlighting their ability to produce sparse networks with algebraic degree distributions, and discusses extensions, generalizations, and research gaps in this area.

Contribution

It provides a comprehensive review of duplication-divergence models, including extensions and generalizations, and discusses open research questions.

Findings

Duplication-divergence models generate sparse graphs with algebraic degree tails.

Extensions of the basic model can capture more complex network features.

The review identifies key research gaps and directions for future work.

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.