Systemic Risk and Vulnerabilities of Bank Networks

Vodenska, Irena; Aoyama, Hideaki; Becker, Alexander P.; Fujiwara, Yoshi; Iyetomi, Hiroshi; Lungu, Eliza-Olivia

doi:10.2139/ssrn.3049976

Cited by 3 publications

(3 citation statements)

References 23 publications

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“…where x i,β = e −η i,β . Combining the two equations (15) and (16) we obtain the following probability distribution over the graphs ensemble…”

Section: Entropy Maximisation In Pcmmentioning

confidence: 99%

“…Network theory [1,2] proved successful [3] in the description and modelling of a wide variety of systems, ranging from the obvious cases of the Internet [4,5], the WWW [6] and social networks [7]. In these settings they formed the evidence on which computational social science is based [8], to cell properties in biology [9] and fMRI imaging in brain analysis [10,11] contributing to the new field of network medicine [12,13], to banks in financial systems [14,15]. Networks come in various shapes, from the simplest case of similar vertices connected by binary edges, to weighted and/or directed networks, to multigraphs where more than one edge can connect two vertices, to bipartite graphs where two distinct sets of vertices are present.…”

Section: Introductionmentioning

confidence: 99%

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Entropy-based randomization of rating networks

2019

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In the last years, due to the great diffusion of e-commerce, online rating platforms quickly became a common tool for purchase recommendations. However, instruments for their analysis did not evolve at the same speed. Indeed, interesting information about users' habits and tastes can be recovered just considering the bipartite network of users and products, in which links have different weights due to the score assigned to items. With respect to other weighted bipartite networks, in these systems we observe a maximum possible weight per link, that limits the variability of the outcomes. In the present article we propose an entropy-based randomisation of (bipartite) rating networks by extending the Configuration Model framework: the randomised network satisfies the constraints of the degree per rating, i.e. the number of given ratings received by the specified product or assigned by the single user. We first show that such a null model is able to reproduce several non-trivial features of the real network better than other null models. Then, using it as a benchmark, we project the information contained in the real system on one of the layers, showing, for instance, the division in communities of music albums due to the taste of customers, or, in movies due the audience.

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“…where x i,β = e −η i,β . Combining the two equations (15) and (16) we obtain the following probability distribution over the graphs ensemble…”

Section: Entropy Maximisation In Pcmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Entropy-based randomization of rating networks

2019

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show abstract

“…In recent years, the network science has drawn increasing attention in a huge class of real-world phenomena 1,2 , such as financial systems [3][4][5][6] , brain activity 7,8 and socioeconomic systems [9][10][11] .…”

Section: Introductionmentioning

confidence: 99%

Maximum entropy approach to link prediction in bipartite networks

Baltakiene,

Baltakys,

Cardamone

et al. 2018

Preprint

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Within network analysis, the analytical maximum entropy framework has been very successful for different tasks as network reconstruction and filtering. In a recent paper, the same framework was used for link-prediction for monopartite networks: link probabilities for all unobserved links in a graph are provided and the most probable links are selected. Here we propose the extension of such an approach to bipartite graphs. We test our method on two real world networks with different topological characteristics. Our performances are compared to state-of-the-art methods, and the results show that our entropy-based approach has a good overall performance.

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Mitigation of cascading failures in complex networks

Smolyak

Levy

Vodenska

et al. 2020

Sci Rep

Self Cite

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Cascading failures in many systems such as infrastructures or financial networks can lead to catastrophic system collapse. We develop here an intuitive, powerful and simple-to-implement approach for mitigation of cascading failures on complex networks based on local network structure. We offer an algorithm to select critical nodes, the protection of which ensures better survival of the network. We demonstrate the strength of our approach compared to various standard mitigation techniques. We show the efficacy of our method on various network structures and failure mechanisms, and finally demonstrate its merit on an example of a real network of financial holdings.

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Systemic Risk and Vulnerabilities of Bank Networks

Cited by 3 publications

References 23 publications

Entropy-based randomization of rating networks

Entropy-based randomization of rating networks

Maximum entropy approach to link prediction in bipartite networks

Mitigation of cascading failures in complex networks

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