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Compute Google's PageRank 5 Times Faster

Posted by timothy on from the hypercustom dept.

Kimberley Burchett writes "CS researchers at Stanford University have developed three new techniques that together could speed up Google's PageRank calculations by a factor of five. An article at ScienceBlog theorizes that "The speed-ups to Google's method may make it realistic to calculate page rankings personalized for an individual's interests or customized to a particular topic.""

4 of 140 comments (clear)

  1. Re:Lets see... by Anonymous Coward · · Score: 5, Informative

    RTA. PageRankings are computed in advance and take several days. A 5x increase in speed means specialized rankings could be computed.

  2. Re:Charge for it by ahaning · · Score: 4, Informative

    But, didn't Google originate out of Stanford? Isn't it reasonable to think that the two are still pretty friendly?

    (Don't you hate it when people speak in questions? Don't you? Huh?)

    --
    Withdrawal before climax is very ineffective and those who try this are usually called "parents."
  3. Re:Lets see... by jesser · · Score: 4, Informative

    Google Search doesn't show hits exactly in the order of page rank. Relevance and other factors also affect order. My biggest page (the one that is my Slashdot URL) is PR7, but there are words on the page for which a lower-rank page beats me, because they're more relevant for that word. Relevance includes how many times the word appears on the page, the HTML context in which it is used, whether pages that link link using the search terms, and the order and nearness of the words in a multi-word search without quotes.

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    The shareholder is always right.
  4. Customized Pagerank by K-Man · · Score: 4, Informative

    Sounds a lot like Kleinberg's HITS algorithm, circa 1997. Try Teoma for a real-world implementation.

    For example, searching a sports-specific Google site for "Giants" would give more importance to pages about the New York or San Francisco Giants and less importance to pages about Jack and the Beanstalk.
    Coincidence time: I used the same example in a presentation a couple of years ago to illustrate how subgroupings can be found for a single search term. Try it on Teoma, and see the various subtopics under "Refine". IIRC each of those is a principal eigenvector of the link matrix.

    Topologically speaking, each principal eigenvector corresponds to a more or less isolated subgraph, eg the subgraph for "San Francisco Giants" is not much connected to the nest of links for "They Might Be Giants", and we get a nice list of subtopics.

    (I once tried to explain this algorithm to my bosses at my former employer, which is why I have so much free time to type this right now.)

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    ---- "If we have to go on with these damned quantum jumps, then I'm sorry that I ever got involved" - Erwin Schrodinger