Growth signals determine the topology of evolving networks

TL;DR

This paper investigates how different growth signals influence the topology of networks generated by the aging nodes model, revealing that time-varying signals produce correlated, clustered networks with power-law degree distributions.

Contribution

It demonstrates that the properties of the growth signal critically shape network structure, emphasizing the importance of incorporating realistic growth signals in network models.

Findings

Networks with power-law degree distributions arise from time-varying growth signals.

Constant growth signals lead to networks that lack correlation and clustering.

Growth signal properties significantly influence network topology.

Abstract

Network science provides an indispensable theoretical framework for studying the structure and function of real complex systems. Different network models are often used for finding the rules that govern their evolution, whereby the correct choice of model details is crucial for obtaining relevant insights. We here study how the structure of networks generated with the aging nodes model depends on the properties of the growth signal. We use different fluctuating signals and compare structural dissimilarities of the networks with those obtained with a constant growth signal. We show that networks with power-law degree distributions, which are obtained with time-varying growth signals, are correlated and clustered, while networks obtained with a constant growth signal are not. Indeed, the properties of the growth signal significantly determine the topology of the obtained networks and thus ought to be considered prominently in models of complex systems.