Disentangling bipartite and core-periphery structure in financial networks

TL;DR

This paper introduces a statistically grounded method using Belief Propagation and entropy maximization to classify and analyze network structures, specifically distinguishing bipartite and core-periphery architectures in financial networks.

Contribution

It develops a novel approach combining SBM and dcSBM models with Belief Propagation to accurately identify network structures and node roles, accounting for degree effects.

Findings

The interbank network is better described as bipartite when degree is considered.

Core-periphery structure appears only with data aggregation over a week.

Method effectively disentangles different network architectures in financial systems.

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.