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PageRank is a way of measuring the importance of website pages. According to Google: PageRank works by counting the number and quality of links to a page to determine a rough estimate of how important the website is. The underlying assumption is that more important websites are likely to receive more links from other websites. [1]
Google PageRank (Google PR) is one of the methods Google uses to determine a page's relevance or importance. Important pages receive a higher PageRank and are more likely to appear at the top of the search results. Google PageRank (PR) is a measure from 0 - 10. Google PageRank is based on backlinks.
HITS, like Page and Brin's PageRank, is an iterative algorithm based on the linkage of the documents on the web. However it does have some major differences: It is processed on a small subset of ‘relevant’ documents (a 'focused subgraph' or base set), instead of the set of all documents as was the case with PageRank.
Fig.1. Google matrix of Wikipedia articles network, written in the bases of PageRank index; fragment of top 200 X 200 matrix elements is shown, total size N=3282257 (from [1]) A Google matrix is a particular stochastic matrix that is used by Google's PageRank algorithm. The matrix represents a graph with edges representing links between pages.
Google PageRank of the main English Wikipedia homepage: . 22 October 2002, 41/100(www.wikipedia.org) 3 November 2002, 7/10 1 December 2002, 8/10 10 January 2003, 8/10 15 March 2003, 7/10 (perhaps Google's algorithm was changed?) 12 April 2003, 7/10 30 May 2003, 7/10 2 October 2003, 8/10 7 November 2003, 8/10 30 January 2004, 8/10 22 October 2004, 8/10 24 December 2004, 8/10 2 July 2005, 9/10 ...
Google's description of its PageRank system (January 1998), for instance, noted that "Google interprets a link from page A to page B as a vote, by page A, for page B." [10] Knowledge of this form of search engine rankings has fueled a portion of the search engine optimization (SEO) industry commonly termed linkspam, where a company attempts to ...
The webgraph is used for: computing the PageRank [5] of the world wide web's pages;; computing the personalized PageRank; [6] detecting webpages of similar topics, through graph-theoretical properties only, like co-citation; [7]
As a result, Wikipedia pages tend to rank well in organic search, and to acquire high PageRank on Google, the most popular search engine as of 2018. These factors create a strong temptation for editors to add linkspam to promote their own sites, whitewash negative articles about themselves or their organizations, or astroturf articles to create ...