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Google's PageRank and Beyond: The Science of Search Engine Rankings
Amy N. Langville,Carl D. Meyer +1 more
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TLDR
Any business seriously interested in improving its rankings in the major search engines can benefit from the clear examples, sample code, and list of resources provided.Abstract:
Why doesn't your home page appear on the first page of search results, even when you query your own name? How do other web pages always appear at the top? What creates these powerful rankings? And how? The first book ever about the science of web page rankings, Google's PageRank and Beyond supplies the answers to these and other questions and more. The book serves two very different audiences: the curious science reader and the technical computational reader. The chapters build in mathematical sophistication, so that the first five are accessible to the general academic reader. While other chapters are much more mathematical in nature, each one contains something for both audiences. For example, the authors include entertaining asides such as how search engines make money and how the Great Firewall of China influences research. The book includes an extensive background chapter designed to help readers learn more about the mathematics of search engines, and it contains several MATLAB codes and links to sample web data sets. The philosophy throughout is to encourage readers to experiment with the ideas and algorithms in the text. Any business seriously interested in improving its rankings in the major search engines can benefit from the clear examples, sample code, and list of resources provided. Many illustrative examples and entertaining asides MATLAB code Accessible and informal style Complete and self-contained section for mathematics reviewread more
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Journal ArticleDOI
Top 10 algorithms in data mining
Xindong Wu,Vipin Kumar,J. Ross Quinlan,Joydeep Ghosh,Qiang Yang,Hiroshi Motoda,Geoffrey J. McLachlan,Angus S. K. Ng,Bing Liu,Philip S. Yu,Zhi-Hua Zhou,Michael Steinbach,David J. Hand,Dan Steinberg +13 more
TL;DR: This paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) in December 2006: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART.
MonographDOI
Spectra and Pseudospectra: The Behavior of Nonnormal Matrices and Operators
Lloyd N. Trefethen,Mark Embree +1 more
TL;DR: In this article, the authors introduce pseudospectra and non-normal matrices, and describe the behavior of nonsymmetric eigenproblems in non-hermitian systems.
Journal ArticleDOI
Naïve Learning in Social Networks and the Wisdom of Crowds
TL;DR: It is shown that all opinions in a large society converge to the truth if and only if the influence of the most influential agent vanishes as the society grows.
Journal ArticleDOI
Vital nodes identification in complex networks
Linyuan Lü,Linyuan Lü,Duanbing Chen,Xiao-Long Ren,Qian-Ming Zhang,Yi-Cheng Zhang,Yi-Cheng Zhang,Tao Zhou +7 more
TL;DR: In this paper, the state-of-the-art algorithms for vital node identification in real networks are reviewed and compared, and extensive empirical analyses are provided to compare well-known methods on disparate real networks.
Journal ArticleDOI
Adiabatic quantum computation
Tameem Albash,Daniel A. Lidar +1 more
TL;DR: In this paper, the equivalence of the adiabatic and circuit models of quantum computation has been proved, and the placement of quantum computations in the more general classification of computational complexity theory is discussed.
References
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Journal ArticleDOI
A mathematical theory of communication
TL;DR: This final installment of the paper considers the case where the signals or the messages or both are continuously variable, in contrast with the discrete nature assumed until now.
Proceedings Article
The PageRank Citation Ranking : Bringing Order to the Web
TL;DR: This paper describes PageRank, a mathod for rating Web pages objectively and mechanically, effectively measuring the human interest and attention devoted to them, and shows how to efficiently compute PageRank for large numbers of pages.
Book
Iterative Methods for Sparse Linear Systems
TL;DR: This chapter discusses methods related to the normal equations of linear algebra, and some of the techniques used in this chapter were derived from previous chapters of this book.
Journal Article
The Anatomy of a Large-Scale Hypertextual Web Search Engine.
Sergey Brin,Lawrence Page +1 more
TL;DR: Google as discussed by the authors is a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext and is designed to crawl and index the Web efficiently and produce much more satisfying search results than existing systems.
Book
Introduction to Modern Information Retrieval
Gerard Salton,Michael J. McGill +1 more
TL;DR: Reading is a need and a hobby at once and this condition is the on that will make you feel that you must read.