Google's Page Rank technology, which is actually developed and owned by Stanford University, giving Google exclusive rights to it, has been analysed to see how effective, considering it's the worlds most effective page ranking algorithm for ranking webpages, it would be at ranking other things, such as scientific papers...

"The pattern of citations between scientific papers forms a network that has remarkable similarities to the network formed by the web. So why not use Google's PageRank, the world's most effective search algorithm to rank these papers in the same way it ranks websites? That's exactly what a couple of US researchers have done for physics papers published by the American Physical Society since 1893 (abstract). The results make interesting reading because almost all of the top ten papers resulted in (or were linked to) Nobel Prizes for their authors. Which means that studying the up-and-coming entries on the list ought to be a good way of predicting future winners. Better get your bets in before the bookies get wind of this."

Mathematical PageRanks (out of 100) for a simple network (PageRanks reported by Google are rescaled logarithmically). Page C has a higher PageRank than Page E, even though it has fewer links to it: the link it has is much higher valued. A web surfer who chooses a random link on every page (but with 15% likelihood jumps to a random page on the whole web) is going to be on Page E for 8.1% of the time. (The 15% likelihood of jumping to an arbitrary page corresponds to a damping factor of 85%.) Without damping, all web surfers would eventually end up on Pages A, B, or C, and all other pages would have PageRank zero. Page A is assumed to link to all pages in the web, because it has no outgoing links.