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Efficient PageRank approximation via graph aggregation

2006
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Information retrieval (Boston)
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We present a framework for approximating random-walk based probability distributions over Web pages using graph aggregation. The basic idea is to partition the graph into classes of quasi-equivalent vertices, to project the page-based random walk to be approximated onto those classes, and to compute the stationary probability distribution of the resulting class-based random walk. From this distribution we can quickly reconstruct a distribution on pages. In particular, our framework can

doi:10.1007/s10791-006-7146-1
fatcat:uf4fxsem5ndxvcvn2nr2wqgiem