{"refrec":{"BRefID":353306,"RR":"<b>Faccin, M.; Schaub, M.T.; Delvenne, J.-C.</b> (2021). State aggregations in Markov chains and block models of networks. <i>Phys. Rev. Lett. 127(7)</i>: 078301. <a href=\"https://dx.doi.org/10.1103/PhysRevLett.127.078301\" target=\"_blank\">https://dx.doi.org/10.1103/PhysRevLett.127.078301</a>","BEntID":351015,"PublicFlag":1,"CheckedFlag":1,"wosflag":1,"vabbflag":1,"RefStringPartII":". <i>Phys. Rev. Lett. 127(7)</i>: 078301. <a href=\"https://dx.doi.org/10.1103/PhysRevLett.127.078301\" target=\"_blank\">https://dx.doi.org/10.1103/PhysRevLett.127.078301</a>","DocTypID":8,"DocType":"Journal article","MarineFlag":0,"FreshFlag":0,"BrackishFlag":0,"TerrestrialFlag":0,"Authorstring":"Faccin, M.; Schaub, M.T.; Delvenne, J.-C.","OrigTitleTranslFlag":0,"Authorstringtrunc":"Faccin, M.; Schaub, M.T.; Delvenne, J.-C.","Englishabstract":"We consider state-aggregation schemes for Markov chains from an information-theoretic perspective. Specifically, we consider aggregating the states of a Markov chain such that the mutual information of the aggregated states separated by T time steps is maximized. We show that for T = 1  this recovers the maximum-likelihood estimator of the degree-corrected stochastic block model as a particular case, which enables us to explain certain features of the likelihood landscape of this generative network model from a dynamical lens. We further highlight how we can uncover coherent, long-range dynamical modules for which considering a timescale T ≫ 1  is essential. 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