Hitting Times in Markov Chains with Restart and their Application to Network Centrality
Provides a theoretical tool for optimizing restart probabilities in Markov processes, benefiting applications in telecommunications, computer science, and physics.
The authors derive an explicit formula for the expected hitting time of a Markov chain with restart, enabling optimization of the restart probability. They demonstrate the formula's utility in network centrality analysis.
Motivated by applications in telecommunications, computer scienceand physics, we consider a discrete-time Markov process withrestart. At each step the process eitherwith a positive probability restarts from a given distribution, orwith the complementary probability continues according to a Markovtransition kernel. The main contribution of the present work is thatwe obtain an explicit expression for the expectation of the hittingtime (to a given target set) of the process with restart.The formula is convenient when considering the problem of optimizationof the expected hitting time with respect to the restart probability.We illustrate our results with two examplesin uncountable and countable state spaces andwith an application to network centrality.