document: Estimating the global pagerank of web communities Localized search engines are small-scale systems that index a particular community on the web . They offer several benefits over their large-scale counterparts in that they are relatively inexpensive to build , and can provide more precise and complete search capability over their relevant domains . One disadvantage such systems have over large-scale search engines is the lack of global PageRank values . Such information is needed to assess the value of pages in the localized search domain within the context of the web as a whole . In this paper , we present well-motivated algorithms to estimate the global PageRank values of a local domain . The algorithms are all highly scalable in that , given a local domain of size n , they use O ( n ) resources that include computation time , bandwidth , and storage . We test our methods across a variety of localized domains , including site-specific domains and topic-specific domains . We demonstrate that by crawling as few as n or 2n additional pages , our methods can give excellent global PageRank estimates .
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