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International Workshop on Self-Organizing Systems

IWSOS 2012: Self-Organizing Systems pp 24–35Cite as

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Triadic Motifs and Dyadic Self-Organization in the World Trade Network

Triadic Motifs and Dyadic Self-Organization in the World Trade Network

  • Tiziano Squartini18,19,20 &
  • Diego Garlaschelli18 
  • Conference paper
  • 1130 Accesses

  • 30 Citations

  • 1 Altmetric

Part of the Lecture Notes in Computer Science book series (LNCCN,volume 7166)

Abstract

In self-organizing networks, topology and dynamics coevolve in a continuous feedback, without exogenous driving. The World Trade Network (WTN) is one of the few empirically well documented examples of self-organizing networks: its topology depends on the GDP of world countries, which in turn depends on the structure of trade. Therefore, understanding the WTN topological properties deviating from randomness provides direct empirical information about the structural effects of self-organization. Here, using an analytical pattern-detection method we have recently proposed, we study the occurrence of triadic ‘motifs’ (three-vertices subgraphs) in the WTN between 1950 and 2000. We find that motifs are not explained by only the in- and out-degree sequences, but they are completely explained if also the numbers of reciprocal edges are taken into account. This implies that the self-organization process underlying the evolution of the WTN is almost completely encoded into the dyadic structure, which strongly depends on reciprocity.

Keywords

  • Gross Domestic Product
  • Null Model
  • Real Network
  • Directed Network
  • Degree Sequence

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Garlaschelli, D., Loffredo, M.I.: Fitness-dependent topological properties of the World Trade Web. Phys. Rev. Lett. 93, 188701 (2004)

    CrossRef  Google Scholar 

  2. Garlaschelli, D., Di Matteo, T., Aste, T., Caldarelli, G., Loffredo, M.I.: Interplay between topology and dynamics in the World Trade Web. Eur. Phys. J. B 57, 159–164 (2007)

    CrossRef  MathSciNet  MATH  Google Scholar 

  3. Garlaschelli, D., Capocci, A., Caldarelli, G.: Self-organized network evolution coupled to extremal dynamics. Nat. Phys. 3, 813–817 (2007)

    CrossRef  Google Scholar 

  4. Squartini, T., Fagiolo, G., Garlaschelli, D.: Randomizing world trade. I. A binary network analysis. Phys. Rev. E 84, 046117 (2011)

    CrossRef  Google Scholar 

  5. Squartini, T., Garlaschelli, D.: Analytical maximum-likelihood method to detect patterns in real networks. New J. Phys. 13, 083001 (2011)

    CrossRef  Google Scholar 

  6. Maslov, S., Sneppen, K., Zaliznyak, A.: Detection of topological patterns in complex networks: correlation profile of the Internet. Physica A 333, 529–540 (2004)

    CrossRef  Google Scholar 

  7. Holland, P., Leinhardt, S.: Sociological Methodology, pp. 1–45. Jossey-Bass, San Francisco (1975)

    Google Scholar 

  8. Park, J., Newman, M.E.J.: Statistical mechanics of networks. Phys. Rev. E 70, 066117 (2004)

    CrossRef  MathSciNet  Google Scholar 

  9. Garlaschelli, D., Loffredo, M.I.: Maximum likelihood: extracting unbiased information from complex networks. Phys. Rev. E 78, 015101(R) (2008)

    CrossRef  MathSciNet  Google Scholar 

  10. Garlaschelli, D., Loffredo, M.I.: Patterns of link reciprocity in directed networks. Phys. Rev. Lett. 93, 268701 (2004)

    CrossRef  Google Scholar 

  11. Garlaschelli, D., Loffredo, M.I.: Multispecies grand-canonical models for networks with reciprocity. Phys. Rev. E 73, 015101(R) (2004)

    CrossRef  MathSciNet  Google Scholar 

  12. Fagiolo, G.: Clustering in complex directed networks. Phys. Rev. E 76, 026107 (2007)

    CrossRef  Google Scholar 

  13. Caldarelli, G.: Scale-free Networks. Complex Webs in Nature and Technology, pp. 35–38. Oxford University Press, Oxford (2007)

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Instituut-Lorentz for Theoretical Physics, Leiden Institute of Physics, University of Leiden, Niels Bohrweg 2, 2333 CA, Leiden, The Netherlands

    Tiziano Squartini & Diego Garlaschelli

  2. Department of Physics, University of Siena, Italy

    Tiziano Squartini

  3. Center for the Study of Complex Systems, via Roma 56, 53100, Siena, Italy

    Tiziano Squartini

Authors
  1. Tiziano Squartini
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  2. Diego Garlaschelli
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Editor information

Editors and Affiliations

  1. Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, P.O. Box 5031, 2600 GA, Delft, The Netherlands

    Fernando A. Kuipers

  2. Department of Telematics, Norwegian University of Science and Technology, O.S. Bragstads plass 2B, 7491, Trondheim, Norway

    Poul E. Heegaard

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© 2012 IFIP International Federation for Information Processing

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Cite this paper

Squartini, T., Garlaschelli, D. (2012). Triadic Motifs and Dyadic Self-Organization in the World Trade Network. In: Kuipers, F.A., Heegaard, P.E. (eds) Self-Organizing Systems. IWSOS 2012. Lecture Notes in Computer Science, vol 7166. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28583-7_3

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  • DOI: https://doi.org/10.1007/978-3-642-28583-7_3

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  • Print ISBN: 978-3-642-28582-0

  • Online ISBN: 978-3-642-28583-7

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