A Survey on PageRank Computing
A Survey on PageRank Computing
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DOI：
10.1080/15427951.2005.10129098
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发表时间：
2005-01-01
影响因子：
--
通讯作者：
Berkhin, Pavel
中科院分区：
文献类型：
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作者：
Berkhin, Pavel
本调查回顾了与PageRank计算相关的研究。PageRank向量的组成部分作为网页的权威权重，独立于其文本内容，仅基于Web的超链接结构。PageRank通常用作Web搜索排名组件。这定义了模型的重要性和PageRank处理的数据结构。即使计算一个PageRank也是一项困难的计算任务。计算许多PageRank是一个更复杂的挑战。最近，在构建个性化PageRank向量集方面投入了大量精力。PageRank也被用于许多不同的应用程序以外的排名。我们感兴趣的理论基础的PageRank公式，在加速PageRank计算，在特定方面的影响，网页图结构的最佳组织的计算，并在PageRank稳定性。我们还审查了替代模型，导致类似于PageRank的权威指数和这种指数在网络搜索以外的应用程序中的作用。我们还讨论了基于链接的搜索个性化，并概述了PageRank基础设施的一些方面，从相关的收敛措施到链接预处理。
This survey reviews the research related to PageRank computing. Components of a PageRank vector serve as authority weights for web pages independent of their textual content, solely based on the hyperlink structure of the web. PageRank is typically used as a web search ranking component. This defines the importance of the model and the data structures that underly PageRank processing. Computing even a single PageRank is a difficult computational task. Computing many PageRanks is a much more complex challenge. Recently, significant effort has been invested in building sets of personalized PageRank vectors. PageRank is also used in many diverse applications other than ranking.We are interested in the theoretical foundations of the PageRank formulation, in the acceleration of PageRank computing, in the effects of particular aspects of web graph structure on the optimal organization of computations, and in PageRank stability. We also review alternative models that lead to authority indices similar to PageRank and the role of such indices in applications other than web search. We also discuss linkbased search personalization and outline some aspects of PageRank infrastructure from associated measures of convergence to link preprocessing.