Model of complex networks based on citation dynamics

TL;DR

This paper introduces a new evolving network model that captures key properties of real-world networks, including disassortativity, and offers a natural interpretation for citation networks.

Contribution

The authors propose a simple, evolving network model that reproduces common real-world network features, especially degree disassortativity, with applications to citation networks.

Findings

Model generates networks with scale-free, small-world, and disassortative properties.

The model provides a natural framework for understanding citation network dynamics.

Results align with observed properties of various real-world networks.

Abstract

Complex networks of real-world systems are believed to be controlled by common phenomena, producing structures far from regular or random. These include scale-free degree distributions, small-world structure and assortative mixing by degree, which are also the properties captured by different random graph models proposed in the literature. However, many (non-social) real-world networks are in fact disassortative by degree. Thus, we here propose a simple evolving model that generates networks with most common properties of real-world networks including degree disassortativity. Furthermore, the model has a natural interpretation for citation networks with different practical applications.