Constraints and Entropy in a Model of Network Evolution

TL;DR

This paper extends the Barabási-Albert scale-free network model to better fit real-world data by incorporating constraints and entropy considerations, deriving the model from thermodynamic principles.

Contribution

It introduces a simple extension to the scale-free model that accounts for degree cut-offs and derives the model from entropy principles, explaining the emergence of constraints.

Findings

Extended model fits real network degree distributions better.

Degree cut-offs are explained by entropy constraints.

Preferential attachment and constraints emerge from thermodynamic principles.

Abstract

Barab\'asi-Albert's `Scale Free' model is the starting point for much of the accepted theory of the evolution of real world communication networks. Careful comparison of the theory with a wide range of real world networks, however, indicates that the model is in some cases, only a rough approximation to the dynamical evolution of real networks. In particular, the exponent $\gamma$ of the power law distribution of degree is predicted by the model to be exactly 3, whereas in a number of real world networks it has values between 1.2 and 2.9. In addition, the degree distributions of real networks exhibit cut offs at high node degree, which indicates the existence of maximal node degrees for these networks. In this paper we propose a simple extension to the `Scale Free' model, which offers better agreement with the experimental data. This improvement is satisfying, but the model still does not explain \emph{why} the attachment probabilities should favor high degree nodes, or indeed how constraints arrive in non-physical networks. Using recent advances in the analysis of the entropy of graphs at the node level we propose a first principles derivation for the `Scale Free' and `constraints' model from thermodynamic principles, and demonstrate that both preferential attachment and constraints could arise as a natural consequence of the second law of thermodynamics.