Entropy rate of non-equilibrium growing networks

TL;DR

This paper introduces the entropy rate for growing networks, analyzing how it differs from static networks and revealing exponential reductions in entropy for certain models, with implications for understanding network complexity and phase transitions.

Contribution

It defines and analytically evaluates the entropy rate of growing network models, highlighting differences from static networks and exploring effects of structural phase transitions.

Findings

Growing networks with linear preferential attachment have exponentially lower entropy than static counterparts.

Entropy rate varies across models with structural phase transitions.

Numerical evidence shows different scaling behaviors of entropy rate above and below phase transitions.

Abstract

New entropy measures have been recently introduced for the quantification of the complexity of networks. Most of these entropy measures apply to static networks or to dynamical processes defined on static complex networks. In this paper we define the entropy rate of growing network models. This entropy rate quantifies how many labeled networks are typically generated by the growing network models. We analytically evaluate the difference between the entropy rate of growing tree network models and the entropy of tree networks that have the same asymptotic degree distribution. We find that the growing networks with linear preferential attachment generated by dynamical models are exponentially less than the static networks with the same degree distribution for a large variety of relevant growing network models. We study the entropy rate for growing network models showing structural phase transitions including models with non-linear preferential attachment. Finally, we bring numerical evidence that the entropy rate above and below the structural phase transitions follow a different scaling with the network size.