Locally Markov walks demonstrate their properties in finite graphs, suggesting important implications for Markov processes.
Locally Markov walks are natural generalizations of classical Markov chains, where instead of a particle moving independently of the past, it decides where to move next depending on the last action performed at the current location. We introduce the concept of locally Markov walks and we describe their stationary distribution and recurrent states, and we prove several properties such as irreducibility and ergodicity. For a particular locally Markov walk‐the uniform unicycle walk on the complete graph‐we investigate the mixing time, and we prove that it exhibits cutoff.
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Kaiser et al. (2026) studied this question.
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