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A Pólya urn approach to information filtering in complex networks

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  • Riccardo Marcaccioli

    (University College London)

  • Giacomo Livan

    (University College London
    London School of Economics and Political Sciences)

Abstract

The increasing availability of data demands for techniques to filter information in large complex networks of interactions. A number of approaches have been proposed to extract network backbones by assessing the statistical significance of links against null hypotheses of random interaction. Yet, it is well known that the growth of most real-world networks is non-random, as past interactions between nodes typically increase the likelihood of further interaction. Here, we propose a filtering methodology inspired by the Pólya urn, a combinatorial model driven by a self-reinforcement mechanism, which relies on a family of null hypotheses that can be calibrated to assess which links are statistically significant with respect to a given network’s own heterogeneity. We provide a full characterization of the filter, and show that it selects links based on a non-trivial interplay between their local importance and the importance of the nodes they belong to.

Suggested Citation

  • Riccardo Marcaccioli & Giacomo Livan, 2019. "A Pólya urn approach to information filtering in complex networks," Nature Communications, Nature, vol. 10(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-08667-3
    DOI: 10.1038/s41467-019-08667-3
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    Cited by:

    1. Rigana, Katerina & Wit, Ernst-Jan Camiel & Cook, Samantha, 2023. "A new way of measuring effects of financial crisis on contagion in currency markets," International Review of Financial Analysis, Elsevier, vol. 90(C).
    2. Katerina Rigana & Ernst-Jan Camiel Wit & Samantha Cook, 2021. "Using Network-based Causal Inference to Detect the Sources of Contagion in the Currency Market," Papers 2112.13127, arXiv.org.
    3. Yanquen, Eduardo & Livan, Giacomo & Montañez-Enriquez, Ricardo & Martinez-Jaramillo, Serafin, 2022. "Measuring systemic risk for bank credit networks: A multilayer approach," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 3(2).
    4. Caceres-Santos, Jonnathan & Rodriguez-Martinez, Anahi & Caccioli, Fabio & Martinez-Jaramillo, Serafin, 2020. "Systemic risk and other interdependencies among banks in Bolivia," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 1(1).
    5. Giuseppe Brandi & T. Di Matteo, 2020. "A new multilayer network construction via Tensor learning," Papers 2004.05367, arXiv.org.
    6. Mehmet Ali Balcı & Larissa M. Batrancea & Ömer Akgüller & Anca Nichita, 2022. "Coarse Graining on Financial Correlation Networks," Mathematics, MDPI, vol. 10(12), pages 1-16, June.
    7. Jeremy D. Turiel & Tomaso Aste, 2019. "P2P Loan acceptance and default prediction with Artificial Intelligence," Papers 1907.01800, arXiv.org.

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