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Interesting article, would have been helpful when I was writing a paper on Google PageRank since it is a markov chain. I do think it would be valuable to talk about equilibrium distributions and how you can determine the tendency of the system


How does PageRank use Markov Chains? That's an interesting idea.


It models the web as a Markov Chain where web pages are states and the transitions are the outgoing links.


Exactly and the actual page rank of the site comes from finding the equilibrium distribution from the transition matrix, which in this case is called a Google matrix.




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