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The Interplay between Microscopic and Mesoscopic Structures in Complex Networks

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  • Jörg Reichardt
  • Roberto Alamino
  • David Saad

Abstract

Understanding a complex network's structure holds the key to understanding its function. The physics community has contributed a multitude of methods and analyses to this cross-disciplinary endeavor. Structural features exist on both the microscopic level, resulting from differences between single node properties, and the mesoscopic level resulting from properties shared by groups of nodes. Disentangling the determinants of network structure on these different scales has remained a major, and so far unsolved, challenge. Here we show how multiscale generative probabilistic exponential random graph models combined with efficient, distributive message-passing inference techniques can be used to achieve this separation of scales, leading to improved detection accuracy of latent classes as demonstrated on benchmark problems. It sheds new light on the statistical significance of motif-distributions in neural networks and improves the link-prediction accuracy as exemplified for gene-disease associations in the highly consequential Online Mendelian Inheritance in Man database.

Suggested Citation

  • Jörg Reichardt & Roberto Alamino & David Saad, 2011. "The Interplay between Microscopic and Mesoscopic Structures in Complex Networks," PLOS ONE, Public Library of Science, vol. 6(8), pages 1-8, August.
  • Handle: RePEc:plo:pone00:0021282
    DOI: 10.1371/journal.pone.0021282
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    References listed on IDEAS

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    1. Chaoming Song & Shlomo Havlin & Hernán A. Makse, 2005. "Self-similarity of complex networks," Nature, Nature, vol. 433(7024), pages 392-395, January.
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    Cited by:

    1. Möller, Simon & Hameister, Heike & Hütt, Marc-Thorsten, 2014. "A genome signature derived from the interplay of word frequencies and symbol correlations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 414(C), pages 216-226.
    2. Jeroen van Lidth de Jeude & Riccardo Di Clemente & Guido Caldarelli & Fabio Saracco & Tiziano Squartini, 2019. "Reconstructing Mesoscale Network Structures," Complexity, Hindawi, vol. 2019, pages 1-13, January.
    3. Yang Tang & Huijun Gao & Wei Zou & Jürgen Kurths, 2012. "Identifying Controlling Nodes in Neuronal Networks in Different Scales," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-13, July.
    4. Haijia Shi & Lei Shi, 2014. "Identifying Emerging Motif in Growing Networks," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-12, June.

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