document: Updating pagerank with iterative aggregation We present an algorithm for updating the PageRank vector ( 1 ) . Due to the scale of the web , Google only updates its famous PageRank vector on a monthly basis . However , the Web changes much more frequently . Drastically speeding the PageRank computation can lead to fresher , more accurate rankings of the webpages retrieved by search engines . It can also make the goal of real-time personalized rankings within reach . On two small subsets of the web , our algorithm updates PageRank using just 25 % and 14 % , respectively , of the time required by the original PageRank algorithm . Our algorithm uses iterative aggregation techniques ( 7 , 8 ) to focus on the slow-converging states of the Markov chain . The most exciting feature of this algorithm is that it can be joined with other PageRank acceleration methods , such as the dangling node lumpability algorithm ( 6 ) , quadratic extrapolation ( 4 ) , and adaptive PageRank ( 3 ) , to realize even greater speedups ( potentially a factor of 60 or more speedup when all algorithms are combined ) . every few weeks . Our solution harnesses the power of iterative aggregation principles for Markov chains to allow for much more frequent updates to the valuable ranking vectors .
doc_bio_tags: B B O O B O O O O O B O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O B I O O O O O O O O O O O O O