State aggregations in Markov chains and block models of networks
In: Physical Review Letters. American Physical Society: Woodbury, N.Y., etc.. ISSN 0031-9007; e-ISSN 1079-7114, more |  |
| 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. |
|
