Skip to main content

IMIS

A new integrated search interface will become available in the next phase of marineinfo.org.
For the time being, please use IMIS to search available data

 

[ report an error in this record ]basket (0): add | show Print this page

State aggregations in Markov chains and block models of networks
Faccin, M.; Schaub, M.T.; Delvenne, J.-C. (2021). State aggregations in Markov chains and block models of networks. Phys. Rev. Lett. 127(7): 078301. https://dx.doi.org/10.1103/PhysRevLett.127.078301
In: Physical Review Letters. American Physical Society: Woodbury, N.Y., etc.. ISSN 0031-9007; e-ISSN 1079-7114, more
Peer reviewed article  

Available in  Authors 

Authors  Top 
  • Faccin, M., more
  • Schaub, M.T.
  • Delvenne, J.-C., more

Abstract
    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. We demonstrate our results using synthetic flows and real-world ocean currents, where we are able to recover the fundamental features of the surface currents of the oceans.

All data in the Integrated Marine Information System (IMIS) is subject to the VLIZ privacy policy Top | Authors