Critical Phenomena in Complex Networks: from Scale-free to Random Networks

TL;DR

This paper investigates critical phenomena in configuration network models with hidden variables, revealing how temperature influences network topology, degree distribution, and phase transitions from disconnected to connected structures.

Contribution

It provides analytical expressions for network properties and demonstrates how temperature controls the transition from scale-free to random graphs and the emergence of a giant component.

Findings

Degree distribution shifts from power-law to Poisson with temperature

Phase transition leads to the formation of a giant component

Network topology undergoes fundamental changes at critical temperature

Abstract

Within the conventional statistical physics framework, we study critical phenomena in a class of configuration network models with hidden variables controlling links between pairs of nodes. We find analytical expressions for the average node degree, the expected number of edges, and the Landau and Helmholtz free energies, as a function of the temperature and number of nodes. We show that the network's temperature is a parameter that controls the average node degree in the whole network and the transition from unconnected graphs to a power-law degree (scale-free) and random graphs. With increasing temperature, the degree distribution is changed from power-law degree distribution, for lower temperatures, to a Poisson-like distribution for high temperatures. We also show that phase transition in the so-called Type A networks leads to fundamental structural changes in the network topology. Below the critical temperature, the graph is completely disconnected. Above the critical temperature, the graph becomes connected, and a giant component appears.