PageRank revisited

被引：29

作者：

Technical University Ilmenau ^{[1
]}

不详 ^{[2
]}

机构：

[1] Faculty for Computer Sciences and Automation, Technical University Ilmenau

来源：

ACM Trans. Internet Technol. | 2006年 / 3卷 / 282-301期

关键词：

Dynamical update; Link-analysis; Markov chain; Pagerank; Personalization; Random surfer; Ranking algorithm; Web graph; Web page scoring; Web search; World Wide Web;

D O I：

10.1145/1151087.1151090

中图分类号：

学科分类号：

摘要：

PageRank, one part of the search engine Google, is one of the most prominent link-based rankings of documents in the World Wide Web. Usually it is described as a Markov chain modeling a specific random surfer. In this article, an alternative representation as a power series is given. Nonetheless, it is possible to interpret the values as probabilities in a random surfer setting, differing from the usual one. Using the new description we restate and extend some results concerning the convergence of the standard iteration used for PageRank. Furthermore we take a closer look at sinks and sources, leading to some suggestions for faster implementations. © 2006 ACM.

引用

页码：282 / 301

页数：19

共 16 条

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