Updating pagerank with iterative aggregation

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 Page Rank acceleration methods, such as the dangling node lumpability algorithm [6], quadratic extrapolation [4], and adaptive Page Rank [3], to realize even greater speedups (potentially a factor of 60 or more speedup when all algorithms are combined).

Citation Context ...determines the ranking of web pages [4, 25].

Computing Page Rank amounts to computing the stationary distribution of a stochastic matrix whose size is now in the billions [21, 22].

Then, using exact aggregation, the problem is reduced by a factor of 4 by lumping all the dangling nodes into one state.

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In =-=[76, 77]-=-, we outlined the connection between the algorithm of Chien et al. This structure has been exploited by several of today’s leading Web search engines, particularly Google and Teoma.

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Web information retrieval is significantly more challenging than traditional wellcontrolled, small document collection information retrieval.

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