ciml - 3:59 pm on Feb 25, 2003 (gmt 0)

Very interesting, msgraph. This document, like the Hilltop and Kleinberg's paper before, looks for a cheaper alternative to contextually sensitive PageRank by selecting a subset of the Web on which to perform link analysis. I agree with Markus that Google appear not to do this yet.

This is a quick comparison of my understanding of these methods, I post it in the hope of having an errors corrected:

PageRank calculates the importance (but not context) of pages by looking at who links to whom, recursively. Google uses PageRank with on page factors and link text to order documents.

Hilltop gets the initial search results and selects those with at least some threshold of external links as 'experts'. It cleverly selects links from the expert pages that are qualified by on-page factors and uses those links (and maybe other information) to order the results.

Kleinberg gets the initial search results and adds pages linked to and from those results. These are used to find hubs and authorities, respectively, by starting with some initial set of values and iterating the flow of forward and back links to and from authorities and hubs, respectively (a principal eigenvector like PageRank).

If I read this patent correctly (I agree with swerve's description), then it gets the initial search results and calculates the simple link popularity of each member of the set, from other members of the set. This 'local' link popularity can be used to re-order the initial results.

mfishy, people who create multiple sites for the purposes of linking are more likely to have ensured separate IP addresses (or class C ranges) than experts who happen to use the same provider (which is likely for some academic and geographical topics). The thing that worries me about these methods is the requirement for non-affiliated sources; smaller initial document sets would be easier to dent IMO.