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Structural comparisons of networks and model-based detection of small-worldness

Author

Listed:
  • Gian Paolo Clemente

    (Catholic University of Milan)

  • Marco Fattore

    (University of Milano-Bicocca)

  • Rosanna Grassi

    (University of Milano-Bicocca)

Abstract

In this paper, we consider the problem of assessing the “level of small-worldness” of a graph and of detecting small-worldness features in real networks. After discussing the limitations of classical approaches, based on the computation of network indicators, we propose a new procedure, which involves the comparison of network structures at different “observation scales”. This allows small-world features to be caught, even if “hidden” deeply into the network structure. Applications of the procedure to both simulated and real data show the effectiveness of the proposal, also in distinguishing between different small-world models and in detecting emerging small-worldness in dynamical networks.

Suggested Citation

  • Gian Paolo Clemente & Marco Fattore & Rosanna Grassi, 2018. "Structural comparisons of networks and model-based detection of small-worldness," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 13(1), pages 117-141, April.
    • Handle: RePEc:spr:jeicoo:v:13:y:2018:i:1:d:10.1007_s11403-017-0202-7
    DOI: 10.1007/s11403-017-0202-7
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    References listed on IDEAS

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    1. A. Barrat & M. Weigt, 2000. "On the properties of small-world network models," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 13(3), pages 547-560, February.
    2. Marco Fattore & Rosanna Grassi, 2014. "Measuring dynamics and structural change of time-dependent socio-economic networks," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(4), pages 1821-1834, July.
    3. Dan Braha & Yaneer Bar-Yam, 2004. "Information Flow Structure in Large-Scale Product Development Organizational Networks," Industrial Organization 0407012, University Library of Munich, Germany.
    4. S. Battiston & M. Catanzaro, 2004. "Statistical properties of corporate board and director networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 38(2), pages 345-352, March.
    5. Caldarelli, Guido & Catanzaro, Michele, 2004. "The corporate boards networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 338(1), pages 98-106.
    6. Lindner, Ines & Strulik, Holger, 2014. "From tradition to modernity: Economic growth in a small world," Journal of Development Economics, Elsevier, vol. 109(C), pages 17-29.
    7. Santella, Paolo & Drago, Carlo & Polo, Andrea & Gagliardi, Enrico, 2009. "A Comparison among the director networks in the main listed companies in France, Germany, Italy, and the United Kingdom," MPRA Paper 16397, University Library of Munich, Germany.
    8. Lucia Bellenzier & Rosanna Grassi, 2014. "Interlocking directorates in Italy: persistent links in network dynamics," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 9(2), pages 183-202, October.
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    Cited by:

    1. Ricardo Giglio & Thomas Lux, 2021. "The Core of the Global Corporate Network," Networks and Spatial Economics, Springer, vol. 21(3), pages 681-705, September.
    2. Bartesaghi, Paolo & Clemente, Gian Paolo & Grassi, Rosanna, 2023. "Taxonomy of cohesion coefficients for weighted and directed multilayer networks," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
    3. Paolo Bartesaghi & Gian Paolo Clemente & Rosanna Grassi, 2020. "Community structure in the World Trade Network based on communicability distances," Papers 2001.06356, arXiv.org, revised Jul 2020.
    4. Paolo Bartesaghi & Gian Paolo Clemente & Rosanna Grassi, 2022. "Community structure in the World Trade Network based on communicability distances," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 17(2), pages 405-441, April.

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    More about this item

    Keywords

    Graph theory; Small-world networks; Graph distance;
    All these keywords.

    JEL classification:

    • C65 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Miscellaneous Mathematical Tools
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation
    • G30 - Financial Economics - - Corporate Finance and Governance - - - General

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