activity
20132026
most citedThe Physics of Financial Networks

283 citations · 802 across the 44 of their papers we have counts for

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Showing 2017Show all

6 papers · 1 filter

cs.SI2017★ 1 cited

A dynamic network model with persistent links and node-specific latent variables, with an application to the interbank market

Piero Mazzarisi, Paolo Barucca, Fabrizio Lillo +1

We propose a dynamic network model where two mechanisms control the probability of a link between two nodes: (i) the existence or absence of this link in the past, and (ii) node-sp…

q-fin.RM2017★ 162 cited

Network models of financial systemic risk: A review

Fabio Caccioli, Paolo Barucca, Teruyoshi Kobayashi

The global financial system can be represented as a large complex network in which banks, hedge funds and other financial institutions are interconnected to each other through visi…

cs.SI2017★ 7 cited

Tackling information asymmetry in networks: a new entropy-based ranking index

Paolo Barucca, Guido Caldarelli, Tiziano Squartini

Information is a valuable asset for agents in socio-economic systems, a significant part of the information being entailed into the very network of connections between agents. The…

q-fin.TR2017★ 1 cited

Behind the price: on the role of agent's reflexivity in financial market microstructure

Paolo Barucca, Fabrizio Lillo

In this chapter we review some recent results on the dynamics of price formation in financial markets and its relations with the efficient market hypothesis. Specifically, we prese…

cond-mat.dis-nn2017★ 8 cited

Exactly Solvable Random Graph Ensemble with Extensively Many Short Cycles

Fabian Aguirre Lopez, Paolo Barucca, Mathilde Fekom +1

We introduce and analyse ensembles of 2-regular random graphs with a tuneable distribution of short cycles. The phenomenology of these graphs depends critically on the scaling of t…

cs.SI2017

Disentangling group and link persistence in Dynamic Stochastic Block models

Paolo Barucca, Fabrizio Lillo, Piero Mazzarisi +1

We study the inference of a model of dynamic networks in which both communities and links keep memory of previous network states. By considering maximum likelihood inference from s…