Critical density for network reconstruction

Andrea Gabrielli, Valentina Macchiati and Diego Garlaschelli

Papers from arXiv.org

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.

Date: 2023-05
New Economics Papers: this item is included in nep-ban, nep-hme, nep-mfd and nep-net
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Published in In: Cantone, D., Pulvirenti, A. (eds) From Computational Logic to Computational Biology. Lecture Notes in Computer Science, vol 14070. Springer, Cham (2024)

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