000074957 001__ 74957
000074957 005__ 20250619084223.0
000074957 0247_ $$2doi$$a10.1103/PhysRevX.8.031038
000074957 0248_ $$2sideral$$a107732
000074957 037__ $$aART-2018-107732
000074957 041__ $$aeng
000074957 100__ $$aLacasa, L.
000074957 245__ $$aMultiplex Decomposition of Non-Markovian Dynamics and the Hidden Layer Reconstruction Problem
000074957 260__ $$c2018
000074957 5060_ $$aAccess copy available to the general public$$fUnrestricted
000074957 5203_ $$aElements composing complex systems usually interact in several different ways, and as such, the interaction architecture is well modeled by a network with multiple layers - a multiplex network - where the system''s complex dynamics is often the result of several intertwined processes taking place at different levels. However, only in a few cases can such multilayered architecture be empirically observed, as one usually only has experimental access to such structure from an aggregated projection. A fundamental challenge is thus to determine whether the hidden underlying architecture of complex systems is better modeled as a single interaction layer or if it results from the aggregation and interplay of multiple layers. Assuming a prior of intralayer Markovian diffusion, here we show that by using local information provided by a random walker navigating the aggregated network, it is possible to determine, in a robust manner, whether these dynamics can be more accurately represented by a single layer or if they are better explained by a (hidden) multiplex structure. In the latter case, we also provide Bayesian methods to estimate the most probable number of hidden layers and the model parameters, thereby fully reconstructing its architecture. The whole methodology enables us to decipher the underlying multiplex architecture of complex systems by exploiting the non-Markovian signatures on the statistics of a single random walk on the aggregated network. In fact, the mathematical formalism presented here extends above and beyond detection of physical layers in networked complex systems, as it provides a principled solution for the optimal decomposition and projection of complex, non-Markovian dynamics into a Markov switching combination of diffusive modes. We validate the proposed methodology with numerical simulations of both (i) random walks navigating hidden multiplex networks (thereby reconstructing the true hidden architecture) and (ii) Markovian and non-Markovian continuous stochastic processes (thereby reconstructing an effective multiplex decomposition where each layer accounts for a different diffusive mode). We also state and prove two existence theorems guaranteeing that an exact reconstruction of the dynamics in terms of these hidden jump-Markov models is always possible for arbitrary finite-order Markovian and fully non-Markovian processes. Finally, we showcase the applicability of the method to experimental recordings from (i) the mobility dynamics of human players in an online multiplayer game and (ii) the dynamics of RNA polymerases at the single-molecule level.
000074957 536__ $$9info:eu-repo/grantAgreement/ES/MINECO/TEC2017-86921-C2-1-R CAIMAN$$9info:eu-repo/grantAgreement/ES/MINECO/TEC2015-69868-C2-1-R$$9info:eu-repo/grantAgreement/ES/MINECO/FIS2017-87519-P$$9info:eu-repo/grantAgreement/ES/MINECO/FIS2014-55867-P$$9info:eu-repo/grantAgreement/ES/DGA/E36-17R
000074957 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000074957 590__ $$a12.211$$b2018
000074957 591__ $$aPHYSICS, MULTIDISCIPLINARY$$b5 / 81 = 0.062$$c2018$$dQ1$$eT1
000074957 592__ $$a6.497$$b2018
000074957 593__ $$aPhysics and Astronomy (miscellaneous)$$c2018$$dQ1
000074957 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000074957 700__ $$aMariño, I.P.
000074957 700__ $$aMiguez, J.
000074957 700__ $$aNicosia, V.
000074957 700__ $$aRoldán, É.
000074957 700__ $$aLisica, A.
000074957 700__ $$aGrill, S.W.
000074957 700__ $$0(orcid)0000-0001-5204-1937$$aGómez-Gardeñes, J.$$uUniversidad de Zaragoza
000074957 7102_ $$12003$$2395$$aUniversidad de Zaragoza$$bDpto. Física Materia Condensa.$$cÁrea Física Materia Condensada
000074957 773__ $$g8, 3 (2018), 031038 [36 pp]$$pPhysical review. X$$tPhysical review. X$$x2160-3308
000074957 8564_ $$s1088136$$uhttps://zaguan.unizar.es/record/74957/files/texto_completo.pdf$$yVersión publicada
000074957 8564_ $$s99584$$uhttps://zaguan.unizar.es/record/74957/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000074957 909CO $$ooai:zaguan.unizar.es:74957$$particulos$$pdriver
000074957 951__ $$a2025-06-19-08:41:25
000074957 980__ $$aARTICLE