Extrapolation methods for accelerating PageRank computations
WWW '03 Proceedings of the 12th international conference on World Wide Web
Distributed PageRank computation based on iterative aggregation-disaggregation methods
Proceedings of the 14th ACM international conference on Information and knowledge management
On computing PageRank via lumping the Google matrix
Journal of Computational and Applied Mathematics
Web Page Rank Prediction with PCA and EM Clustering
WAW '09 Proceedings of the 6th International Workshop on Algorithms and Models for the Web-Graph
Incremental spectral clustering by efficiently updating the eigen-system
Pattern Recognition
Journal of Computational and Applied Mathematics
Parallel SimRank computation on large graphs with iterative aggregation
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Fast incremental and personalized PageRank
Proceedings of the VLDB Endowment
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Dynamic pagerank using evolving teleportation
WAW'12 Proceedings of the 9th international conference on Algorithms and Models for the Web Graph
NCDawareRank: a novel ranking method that exploits the decomposable structure of the web
Proceedings of the sixth ACM international conference on Web search and data mining
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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.