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The geometric nature of weights in real complex networks

Author

Listed:
  • Antoine Allard

    (Departament de Física de la Matèria Condensada, Universitat de Barcelona
    Universitat de Barcelona Institute of Complex Systems (UBICS))

  • M. Ángeles Serrano

    (Departament de Física de la Matèria Condensada, Universitat de Barcelona
    Universitat de Barcelona Institute of Complex Systems (UBICS)
    Institució Catalana de Recerca i Estudis Avançats (ICREA))

  • Guillermo García-Pérez

    (Departament de Física de la Matèria Condensada, Universitat de Barcelona
    Universitat de Barcelona Institute of Complex Systems (UBICS))

  • Marián Boguñá

    (Departament de Física de la Matèria Condensada, Universitat de Barcelona
    Universitat de Barcelona Institute of Complex Systems (UBICS))

Abstract

The topology of many real complex networks has been conjectured to be embedded in hidden metric spaces, where distances between nodes encode their likelihood of being connected. Besides of providing a natural geometrical interpretation of their complex topologies, this hypothesis yields the recipe for sustainable Internet’s routing protocols, sheds light on the hierarchical organization of biochemical pathways in cells, and allows for a rich characterization of the evolution of international trade. Here we present empirical evidence that this geometric interpretation also applies to the weighted organization of real complex networks. We introduce a very general and versatile model and use it to quantify the level of coupling between their topology, their weights and an underlying metric space. Our model accurately reproduces both their topology and their weights, and our results suggest that the formation of connections and the assignment of their magnitude are ruled by different processes.

Suggested Citation

  • Antoine Allard & M. Ángeles Serrano & Guillermo García-Pérez & Marián Boguñá, 2017. "The geometric nature of weights in real complex networks," Nature Communications, Nature, vol. 8(1), pages 1-8, April.
    • Handle: RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_ncomms14103
    DOI: 10.1038/ncomms14103
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    Cited by:

    1. Robert Jankowski & Antoine Allard & Marián Boguñá & M. Ángeles Serrano, 2023. "The D-Mercator method for the multidimensional hyperbolic embedding of real networks," Nature Communications, Nature, vol. 14(1), pages 1-11, December.

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