000095581 001__ 95581 000095581 005__ 20210902121745.0 000095581 0247_ $$2doi$$a10.1137/19M1302041 000095581 0248_ $$2sideral$$a119787 000095581 037__ $$aART-2020-119787 000095581 041__ $$aeng 000095581 100__ $$aBartesaghi, P. 000095581 245__ $$aRisk-dependent centrality in economic and financial networks 000095581 260__ $$c2020 000095581 5060_ $$aAccess copy available to the general public$$fUnrestricted 000095581 5203_ $$aNode centrality is one of the most important and widely used concepts in the study of complex networks. Here, we extend the paradigm of node centrality in financial and economic networks to consider the changes of node "importance""produced not only by the variation of the topology of the system but also as a consequence of the external levels of risk to which the network as a whole is subjected. Starting from the "Susceptible-Infected""(SI) model of epidemics and its relation to the communicability functions of networks, we develop a series of risk-dependent centralities for nodes in (financial and economic) networks. We analyze here some of the most important mathematical properties of these risk-dependent centrality measures. In particular, we study the newly observed phenomenon of ranking interlacement, by means of which two entities may interlace their ranking positions in terms of risk in the network as a consequence of the change in the external conditions only, i.e., without any change in the topology. We test the risk-dependent centralities by studying two realworld systems: The network generated by collecting assets of the S\&P 100 and the corporate board network of the U.S.Top companies, according to Forbes in 1999. We found that a high position in the ranking of the analyzed financial companies according to their risk-dependent centrality corresponds to companies more sensitive to the external market variations during the periods of crisis. 000095581 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/ 000095581 590__ $$a1.877$$b2020 000095581 591__ $$aBUSINESS, FINANCE$$b69 / 108 = 0.639$$c2020$$dQ3$$eT2 000095581 591__ $$aMATHEMATICS, INTERDISCIPLINARY APPLICATIONS$$b62 / 108 = 0.574$$c2020$$dQ3$$eT2 000095581 591__ $$aSOCIAL SCIENCES, MATHEMATICAL METHODS$$b31 / 52 = 0.596$$c2020$$dQ3$$eT2 000095581 592__ $$a1.25$$b2020 000095581 593__ $$aApplied Mathematics$$c2020$$dQ1 000095581 593__ $$aNumerical Analysis$$c2020$$dQ1 000095581 593__ $$aFinance$$c2020$$dQ1 000095581 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion 000095581 700__ $$aBenzi, M. 000095581 700__ $$aClemente, G.P. 000095581 700__ $$aGrassi, R. 000095581 700__ $$0(orcid)0000-0002-3066-7418$$aEstrada, Ernesto 000095581 773__ $$g11, 2 (2020), 526-565$$pSIAM j. financial math.$$tSIAM Journal on Financial Mathematics$$x1945-497X 000095581 8564_ $$s8231534$$uhttps://zaguan.unizar.es/record/95581/files/texto_completo.pdf$$yVersión publicada 000095581 8564_ $$s451899$$uhttps://zaguan.unizar.es/record/95581/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada 000095581 909CO $$ooai:zaguan.unizar.es:95581$$particulos$$pdriver 000095581 951__ $$a2021-09-02-09:45:41 000095581 980__ $$aARTICLE