# State Aggregations in Markov Chains and Block Models of Networks.

@article{Faccin2021StateAI, title={State Aggregations in Markov Chains and Block Models of Networks.}, author={Mauro Faccin and Michael T. Schaub and Jean-Charles Delvenne}, journal={Physical review letters}, year={2021}, volume={127 7}, pages={ 078301 } }

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…

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