The $25,000,000,000 eigenvector: The linear algebra behind google
The $25,000,000,000 eigenvector: The linear algebra behind google
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DOI：
10.1137/050623280
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发表时间：
2006-09-01
期刊：
影响因子：
10.2
通讯作者：
Leise, Tanya
中科院分区：
文献类型：
--
作者：
Bryan, Kurt;Leise, Tanya
谷歌的成功在很大程度上得益于其PageRank算法，该算法根据加权链接矩阵的特征向量对网页的重要性进行排名。PageRank公式的分析为线性代数课程提供了一个很好的应用主题。教师可以将这篇文章作为一个项目分配给更高级的学生，或者花一到两节课的时间来介绍材料和布置的练习作业。这份材料也补充了矩阵代数中马尔可夫链的讨论。支持这一材料的枫叶和数学文件可以在www.Rose-liulman.edu/类似于布莱恩的网站上找到。
Google's success derives in large part from its PageRank algorithm, which ranks the importance of web pages according to an eigenvector of a weighted link matrix. Analysis of the PageRank formula provides a wonderful applied topic for a linear algebra course. Instructors may assign this article as a project to more advanced students or spend one or two lectures presenting the material with assigned homework from the exercises. This material also complements the discussion of Markov chains in matrix algebra. Maple and Mathematica files supporting this material can be found at www.rose-liulman.edu/similar to bryan.