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SpecNet: A Spatial Network Algorithm that Generates a Wide Range of Specific Structures

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  • Jenny Lennartsson
  • Nina Håkansson
  • Uno Wennergren
  • Annie Jonsson

Abstract

Network measures are used to predict the behavior of different systems. To be able to investigate how various structures behave and interact we need a wide range of theoretical networks to explore. Both spatial and non-spatial methods exist for generating networks but they are limited in the ability of producing wide range of network structures. We extend an earlier version of a spatial spectral network algorithm to generate a large variety of networks across almost all the theoretical spectra of the following network measures: average clustering coefficient, degree assortativity, fragmentation index, and mean degree. We compare this extended spatial spectral network-generating algorithm with a non-spatial algorithm regarding their ability to create networks with different structures and network measures. The spatial spectral network-generating algorithm can generate networks over a much broader scale than the non-spatial and other known network algorithms. To exemplify the ability to regenerate real networks, we regenerate networks with structures similar to two real Swedish swine transport networks. Results show that the spatial algorithm is an appropriate model with correlation coefficients at 0.99. This novel algorithm can even create negative assortativity and managed to achieve assortativity values that spans over almost the entire theoretical range.

Suggested Citation

  • Jenny Lennartsson & Nina Håkansson & Uno Wennergren & Annie Jonsson, 2012. "SpecNet: A Spatial Network Algorithm that Generates a Wide Range of Specific Structures," PLOS ONE, Public Library of Science, vol. 7(8), pages 1-10, August.
  • Handle: RePEc:plo:pone00:0042679
    DOI: 10.1371/journal.pone.0042679
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    References listed on IDEAS

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    1. M. C. Boily & Z. Asghar & T. Garske & A. C. Ghani & R. Poulin, 2007. "Influence of Selected Formation Rules for Finite Population Networks with Fixed Macrostructures: Implications for Individual-Based Model of Infectious Diseases," Mathematical Population Studies, Taylor & Francis Journals, vol. 14(4), pages 237-267, November.
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    Cited by:

    1. Clingingsmith, David, 2017. "Are the World's Languages Consolidating? The Dynamics and Distribution of Language Populations," SocArXiv et37r, Center for Open Science.

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