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Disentangling bipartite and core-periphery structure in financial networks

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  • Barucca, Paolo
  • Lillo, Fabrizio

Abstract

A growing number of systems are represented as networks whose architecture conveys significant information and determines many of their properties. Examples of network architecture include modular, bipartite, and core-periphery structures. However inferring the network structure is a non trivial task and can depend sometimes on the chosen null model. Here we propose a method for classifying network structures and ranking its nodes in a statistically well-grounded fashion. The method is based on the use of Belief Propagation for learning through Entropy Maximization on both the Stochastic Block Model (SBM) and the degree-corrected Stochastic Block Model (dcSBM). As a specific application we show how the combined use of the two ensembles—SBM and dcSBM—allows to disentangle the bipartite and the core-periphery structure in the case of the e-MID interbank network. Specifically we find that, taking into account the degree, this interbank network is better described by a bipartite structure, while using the SBM the core-periphery structure emerges only when data are aggregated for more than a week.

Suggested Citation

  • Barucca, Paolo & Lillo, Fabrizio, 2016. "Disentangling bipartite and core-periphery structure in financial networks," Chaos, Solitons & Fractals, Elsevier, vol. 88(C), pages 244-253.
    • Handle: RePEc:eee:chsofr:v:88:y:2016:i:c:p:244-253
    DOI: 10.1016/j.chaos.2016.02.004
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    References listed on IDEAS

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