We study mean cover time on the graph - the average number of steps needed by a random walker to visit all nodes. An appropriate Markov chain model is proposed which allows for the exact calculation of the mean cover time. Due to the exponential increase of the number of states in the Markov chain, as the number of nodes grows, we study only graphs with up to a dozen nodes. Theoretically obtained values were confirmed with numerical simulations for graphs with different topologies. It was also found that for the graphs considered, the mean cover time is smaller for nodes that have smaller eigenvector centrality.
Data Visualization, Higher Education, Machine Learning
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