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Harmony in the small-world

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  • Marchiori, Massimo
  • Latora, Vito

Abstract

The small-world phenomenon, popularly known as six degrees of separation, has been mathematically formalized by Watts and Strogatz in a study of the topological properties of a network. Small-world networks are defined in terms of two quantities: they have a high clustering coefficient C like regular lattices and a short characteristic path length L typical of random networks. Physical distances are of fundamental importance in applications to real cases; nevertheless, this basic ingredient is missing in the original formulation. Here, we introduce a new concept, the connectivity length D, that gives harmony to the whole theory. D can be evaluated on a global and on a local scale and plays in turn the role of L and 1/C. Moreover, it can be computed for any metrical network and not only for the topological cases. D has a precise meaning in terms of information propagation and describes in a unified way, both the structural and the dynamical aspects of a network: small-worlds are defined by a small global and local D, i.e., by a high efficiency in propagating information both on a local and global scale. The neural system of the nematode C. elegans, the collaboration graph of film actors, and the oldest US subway system, can now be studied also as metrical networks and are shown to be small-worlds.

Suggested Citation

  • Marchiori, Massimo & Latora, Vito, 2000. "Harmony in the small-world," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 285(3), pages 539-546.
  • Handle: RePEc:eee:phsmap:v:285:y:2000:i:3:p:539-546
    DOI: 10.1016/S0378-4371(00)00311-3
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    Citations

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    Cited by:

    1. Li, Wenyuan & Lin, Yongjing & Liu, Ying, 2007. "The structure of weighted small-world networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 376(C), pages 708-718.
    2. Massimo Marchiori & Lino Possamai, 2020. "Strategies of Success for Social Networks: Mermaids and Temporal Evolution," Future Internet, MDPI, vol. 12(2), pages 1-30, February.
    3. Mark J. O. Bagley, 2019. "Networks, geography and the survival of the firm," Journal of Evolutionary Economics, Springer, vol. 29(4), pages 1173-1209, September.
    4. Liu, Shuli & Wan, Yulai & Zhang, Anming, 2020. "Does China’s high-speed rail development lead to regional disparities? A network perspective," Transportation Research Part A: Policy and Practice, Elsevier, vol. 138(C), pages 299-321.
    5. Bruno Codenotti & Luca Foschini, 2002. "Small Worlds," LEM Papers Series 2002/11, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    6. Rui Ding & Norsidah Ujang & Hussain Bin Hamid & Mohd Shahrudin Abd Manan & Rong Li & Safwan Subhi Mousa Albadareen & Ashkan Nochian & Jianjun Wu, 2019. "Application of Complex Networks Theory in Urban Traffic Network Researches," Networks and Spatial Economics, Springer, vol. 19(4), pages 1281-1317, December.
    7. B. G. Tóth, 2021. "The effect of attacks on the railway network of Hungary," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 29(2), pages 567-587, June.
    8. Sziklai, Balázs R., 2021. "Ranking institutions within a discipline: The steep mountain of academic excellence," Journal of Informetrics, Elsevier, vol. 15(2).
    9. Liu, Shuli & Wan, Yulai & Ha, Hun-Koo & Yoshida, Yuichiro & Zhang, Anming, 2019. "Impact of high-speed rail network development on airport traffic and traffic distribution: Evidence from China and Japan," Transportation Research Part A: Policy and Practice, Elsevier, vol. 127(C), pages 115-135.
    10. Marc Barthélémy & Michele Campagna & Alessandro Chessa & Andrea De Montis & Alessandro Vespignani, 2005. "Emergent topological and dynamical properties of a real inter-municipal commuting network - perspectives for policy-making and planning," ERSA conference papers ersa05p607, European Regional Science Association.
    11. Massimiliano Turchetto & Michele Bellingeri & Roberto Alfieri & Ngoc-Kim-Khanh Nguyen & Quang Nguyen & Davide Cassi, 2023. "Random Walks-Based Node Centralities to Attack Complex Networks," Mathematics, MDPI, vol. 11(23), pages 1-20, November.
    12. Pramod C. Mane & Nagarajan Krishnamurthy & Kapil Ahuja, 2019. "Formation of Stable and Efficient Social Storage Cloud," Games, MDPI, vol. 10(4), pages 1-17, November.
    13. Ghedini, Cinara G. & Ribeiro, Carlos H.C., 2011. "Rethinking failure and attack tolerance assessment in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(23), pages 4684-4691.
    14. Tajana Ban Kirigin & Sanda Bujačić Babić, 2024. "Semi-Local Integration Measure for Directed Graphs," Mathematics, MDPI, vol. 12(7), pages 1-17, April.
    15. Dassisti, M. & Carnimeo, L., 2013. "A small-world methodology of analysis of interchange energy-networks: The European behaviour in the economical crisis," Energy Policy, Elsevier, vol. 63(C), pages 887-899.
    16. Smith, Thomas Bryan, 2021. "Gang crackdowns and offender centrality in a countywide co-offending network: A networked evaluation of Operation Triple Beam," Journal of Criminal Justice, Elsevier, vol. 73(C).
    17. Sheng, Long & Li, Chunguang, 2009. "English and Chinese languages as weighted complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(12), pages 2561-2570.
    18. Poggi, Ambra & Natale, Piergiovanna, 2020. "Learning by hiring, network centrality and within-firm wage dispersion," Labour Economics, Elsevier, vol. 67(C).
    19. Lordan, Oriol & Sallan, Jose M. & Simo, Pep, 2014. "Study of the topology and robustness of airline route networks from the complex network approach: a survey and research agenda," Journal of Transport Geography, Elsevier, vol. 37(C), pages 112-120.
    20. Moreno Bonaventura & Luca Maria Aiello & Daniele Quercia & Vito Latora, 2021. "Predicting urban innovation from the US Workforce Mobility Network," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-9, December.
    21. Sun, Yeran & Mburu, Lucy & Wang, Shaohua, 2016. "Analysis of community properties and node properties to understand the structure of the bus transport network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 523-530.
    22. Pramod C. Mane & Nagarajan Krishnamurthy & Kapil Ahuja, 2021. "Resource Availability in the Social Cloud: An Economics Perspective," Papers 2102.01071, arXiv.org.
    23. Christian Esposito & Marco Gortan & Lorenzo Testa & Francesca Chiaromonte & Giorgio Fagiolo & Andrea Mina & Giulio Rossetti, 2022. "Venture capital investments through the lens of network and functional data analysis," LEM Papers Series 2022/07, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.

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