This applet demonstrates the PageRank algorithm on a simple network. It is an iterative algorithm, that converges to the final score for each node after just a few iterations. At each iteration, a node distributes (1-alpha)*(its current score) equally (in the case of an unweighted network) among their outgoing links. It distributes alpha*(its current score) equally among all nodes (this corresponds to the random jump or teleportation probability). If you're think of the random web surfer browsing pages, with probability (1-alpha) they'll click on a link in the current page, and with probability alpha they'll start afresh on a random page.
What you can do:
You can modify the teleportation probability. What happens to the distribution of scores as you increase it?
You can decide whether or not to allow sinks. If you allow sinks, then once the websurfer reaches a page with no outlinks, it will stay there with probability (1-alpha) and jump to a random page with probability alpha. If you do not allow sinks, they will escape right away to a random page.
To read more about the PageRank algorithm:
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