Nonextensive information entropy for stochastic networks

TL;DR

This paper explores how maximizing nonextensive information entropy can explain the emergence of scale-free, power-law distributions in complex stochastic networks across various natural and social systems.

Contribution

It introduces a nonextensive entropy framework that accounts for the scale-free distributions observed in complex networks, extending traditional information theory.

Findings

Nonextensive entropy leads to power-law distributions

Scale-free networks can be derived from entropy maximization

The approach applies to biological, economic, and informational systems

Abstract

Nature is full of random networks of complex topology describing such apparently disparate systems as biological, economical or informatical ones. Their most characteristic feature is the apparent scale-free character of interconnections between nodes. Using an information theory approach, we show that maximalization of information entropy leads to a wide spectrum of possible types of distributions including, in the case of nonextensive information entropy, the power-like scale-free distributions characteristic of complex systems.