PageRank, one part of the search engine Google, is one of the most prominent link-based rankings of documents in the World Wide Web. Usually it is described as a Markov chain modeling a specific random surfer. In this article, an alternative representation as a power series is given. Nonetheless, it is possible to interpret the values as probabilities in a random surfer setting, differing from the usual one.Using the new description we restate and extend some results concerning the convergence of the standard iteration used for PageRank. Furthermore we take a closer look at sinks and sources, leading to some suggestions for faster implementations.
%0 Journal Article
%1 Brinkmeier:2006:PR:1151087.1151090
%A Brinkmeier, Michael
%C New York, NY, USA
%D 2006
%I ACM
%J ACM Trans. Internet Technol.
%K calculation dangling link links pagerank sinks
%N 3
%P 282--301
%R 10.1145/1151087.1151090
%T PageRank revisited
%U http://doi.acm.org/10.1145/1151087.1151090
%V 6
%X PageRank, one part of the search engine Google, is one of the most prominent link-based rankings of documents in the World Wide Web. Usually it is described as a Markov chain modeling a specific random surfer. In this article, an alternative representation as a power series is given. Nonetheless, it is possible to interpret the values as probabilities in a random surfer setting, differing from the usual one.Using the new description we restate and extend some results concerning the convergence of the standard iteration used for PageRank. Furthermore we take a closer look at sinks and sources, leading to some suggestions for faster implementations.
@article{Brinkmeier:2006:PR:1151087.1151090,
abstract = {PageRank, one part of the search engine Google, is one of the most prominent link-based rankings of documents in the World Wide Web. Usually it is described as a Markov chain modeling a specific random surfer. In this article, an alternative representation as a power series is given. Nonetheless, it is possible to interpret the values as probabilities in a random surfer setting, differing from the usual one.Using the new description we restate and extend some results concerning the convergence of the standard iteration used for PageRank. Furthermore we take a closer look at sinks and sources, leading to some suggestions for faster implementations.},
acmid = {1151090},
added-at = {2013-02-21T17:16:57.000+0100},
address = {New York, NY, USA},
author = {Brinkmeier, Michael},
biburl = {https://www.bibsonomy.org/bibtex/27c13a0250737f37fc11cf9ff9b7da3dc/gzymeri},
doi = {10.1145/1151087.1151090},
interhash = {c5ad30b2379993a46d082508035c15c6},
intrahash = {7c13a0250737f37fc11cf9ff9b7da3dc},
issn = {1533-5399},
issue_date = {August 2006},
journal = {ACM Trans. Internet Technol.},
keywords = {calculation dangling link links pagerank sinks},
month = aug,
number = 3,
numpages = {20},
pages = {282--301},
publisher = {ACM},
timestamp = {2013-02-21T17:16:57.000+0100},
title = {PageRank revisited},
url = {http://doi.acm.org/10.1145/1151087.1151090},
volume = 6,
year = 2006
}