The exponential degree distribution in complex networks: Nonequilibrium network theory, numerical simulation and empirical data
Abstract
The exponential degree distribution has been found in many real world complex networks, based on which, the random growing process has been introduced to analyze the formation principle of such kinds of networks. Inspired from the nonequilibrium network theory, we construct the network according to two mechanisms: growing and adjacent random attachment. By using the KolmogorovSmirnov Test (KST), for the same number of nodes and edges, we find the simulation results are remarkably consistent with the predictions of the nonequilibrium network theory, and also surprisingly match the empirical databases, such as the Worldwide Marine Transportation Network (WMTN), the Email Network of University at Rovira i Virgili (ENURV) in Spain and the North American Power Grid Network (NAPGN). Our work may shed light on interpreting the exponential degree distribution and the evolution mechanism of the complex networks.
 Publication:

Physica A Statistical Mechanics and its Applications
 Pub Date:
 April 2011
 DOI:
 10.1016/j.physa.2010.12.029
 Bibcode:
 2011PhyA..390.1481D