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A clustering coefficient for complete weighted networks

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  • MCASSEY, MICHAEL P.
  • BIJMA, FETSJE

Abstract

The clustering coefficient is typically used as a measure of the prevalence of node clusters in a network. Various definitions for this measure have been proposed for the cases of networks having weighted edges which may or not be directed. However, these techniques consistently assume that only a subset of all possible edges is present in the network, whereas there are weighted networks of interest in which all possible edges are present, that is, complete weighted networks. For this situation, the concept of clustering is redefined, and computational techniques are presented for computing an associated clustering coefficient for complete weighted undirected or directed networks. The performance of this new definition is compared with that of current clustering definitions when extended to complete weighted networks.

Suggested Citation

  • Mcassey, Michael P. & Bijma, Fetsje, 2015. "A clustering coefficient for complete weighted networks," Network Science, Cambridge University Press, vol. 3(2), pages 183-195, June.
    • Handle: RePEc:cup:netsci:v:3:y:2015:i:02:p:183-195_00
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    Cited by:

    1. Leonidov, Andrey & Serebryannikova, Ekaterina, 2019. "Dynamical topology of highly aggregated input–output networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 518(C), pages 234-252.
    2. João Amador & Sónia Cabral & Rossana Mastrandrea & Franco Ruzzenenti, 2018. "Who’s Who in Global Value Chains? A Weighted Network Approach," Open Economies Review, Springer, vol. 29(5), pages 1039-1059, November.
    3. Clemente, G.P. & Grassi, R., 2018. "Directed clustering in weighted networks: A new perspective," Chaos, Solitons & Fractals, Elsevier, vol. 107(C), pages 26-38.
    4. Gian Paolo Clemente & Rosanna Grassi & Asmerilda Hitaj, 2022. "Smart network based portfolios," Annals of Operations Research, Springer, vol. 316(2), pages 1519-1541, September.
    5. Gian Paolo Clemente & Rosanna Grassi & Asmerilda Hitaj, 2018. "Asset allocation: new evidence through network approaches," Papers 1810.09825, arXiv.org.
    6. Herteliu, Claudiu & Levantesi, Susanna & Rotundo, Giulia, 2021. "Network analysis of pension funds investments," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 579(C).
    7. Gian Paolo Clemente & Rosanna Grassi & Asmerilda Hitaj, 2019. "Smart network based portfolios," Papers 1907.01274, arXiv.org.
    8. Sun, Hang, 2016. "Crisis-Contingent Dynamics of Connectedness: An SVAR-Spatial-Network “Tripod” Model with Thresholds," Research Memorandum 032, Maastricht University, Graduate School of Business and Economics (GSBE).
    9. Gian Paolo Clemente & Rosanna Grassi & Asmerilda Hitaj, 2021. "Asset allocation: new evidence through network approaches," Annals of Operations Research, Springer, vol. 299(1), pages 61-80, April.
    10. Roy Cerqueti & Gian Paolo Clemente & Rosanna Grassi, 2019. "A Network-Based Measure of the Socio-Economic Roots of the Migration Flows," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 146(1), pages 187-204, November.

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