Exactly scale-free scale-free networks

L. Zhang, Michael Small, Kevin Judd

    Research output: Contribution to journalArticle

    8 Citations (Scopus)
    523 Downloads (Pure)

    Abstract

    © 2015 Elsevier B.V. All rights reserved. Many complex natural and physical systems exhibit patterns of interconnection that conform, approximately, to a network structure referred to as scale-free. Preferential attachment is one of many algorithms that have been introduced to model the growth and structure of scale-free networks. With so many different models of scale-free networks it is unclear what properties of scale-free networks are typical, and what properties are peculiarities of a particular growth or construction process. We propose a simple maximum entropy process which provides the best representation of what are typical properties of scale-free networks, and provides a standard against which real and algorithmically generated networks can be compared. As an example we consider preferential attachment and find that this particular growth model does not yield typical realizations of scale-free networks. In particular, the widely discussed "fragility" of scale-free networks is actually found to be due to the peculiar "hub-centric" structure of preferential attachment networks. We provide a method to generate or remove this latent hub-centric bias - thereby demonstrating exactly which features of preferential attachment networks are atypical of the broader class of scale-free networks. We are also able to statistically demonstrate whether real networks are typical realizations of scale-free networks, or networks with that particular degree distribution; using a new surrogate generation method for complex networks, exactly analogous the widely used surrogate tests of nonlinear time series analysis.
    Original languageEnglish
    Pages (from-to)182-197
    JournalPhysica A: Statistical Mechanics and its Applications
    Volume433
    Early online date7 Apr 2015
    DOIs
    Publication statusPublished - 1 Sep 2015

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