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Critical density for network reconstruction

Author

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  • Andrea Gabrielli
  • Valentina Macchiati
  • Diego Garlaschelli
Abstract
The structure of many financial networks is protected by privacy and has to be inferred from aggregate observables. Here we consider one of the most successful network reconstruction methods, producing random graphs with desired link density and where the observed constraints (related to the market size of each node) are replicated as averages over the graph ensemble, but not in individual realizations. We show that there is a minimum critical link density below which the method exhibits an `unreconstructability' phase where at least one of the constraints, while still reproduced on average, is far from its expected value in typical individual realizations. We establish the scaling of the critical density for various theoretical and empirical distributions of interbank assets and liabilities, showing that the threshold differs from the critical densities for the onset of the giant component and of the unique component in the graph. We also find that, while dense networks are always reconstructable, sparse networks are unreconstructable if their structure is homogeneous, while they can display a crossover to reconstructability if they have an appropriate core-periphery or heterogeneous structure. Since the reconstructability of interbank networks is related to market clearing, our results suggest that central bank interventions aimed at lowering the density of links should take network structure into account to avoid unintentional liquidity crises where the supply and demand of all financial institutions cannot be matched simultaneously.

Suggested Citation

  • Andrea Gabrielli & Valentina Macchiati & Diego Garlaschelli, 2023. "Critical density for network reconstruction," Papers 2305.17285, arXiv.org.
    • Handle: RePEc:arx:papers:2305.17285
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    References listed on IDEAS

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    1. Giulio Cimini & Tiziano Squartini & Diego Garlaschelli & Andrea Gabrielli, 2014. "Systemic risk analysis in reconstructed economic and financial networks," Papers 1411.7613, arXiv.org, revised May 2015.
    2. Marco Bardoscia & Stefano Battiston & Fabio Caccioli & Guido Caldarelli, 2015. "DebtRank: A Microscopic Foundation for Shock Propagation," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-13, June.
    3. Tiziano Squartini & Guido Caldarelli & Giulio Cimini & Andrea Gabrielli & Diego Garlaschelli, 2018. "Reconstruction methods for networks: the case of economic and financial systems," Papers 1806.06941, arXiv.org.
    4. Marco Bardoscia & Paolo Barucca & Stefano Battiston & Fabio Caccioli & Giulio Cimini & Diego Garlaschelli & Fabio Saracco & Tiziano Squartini & Guido Caldarelli, 2021. "The Physics of Financial Networks," Papers 2103.05623, arXiv.org.
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