What is PageRank? How PageRank is calculated using PageRank algorithm? Explain with suitable example.

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 PageRank

The year 1998 was an important year for Weblink analysis and Web search. Both the PageRank and the HITS algorithms were reported in that year. A HIT was presented by Jon Kleinberg in January 1998 at the Ninth Annual ACM-SIAM Symposium on Discrete Algorithms. PageRank was presented by Sergey Brin and Larry Page at the Seventh International World Wide Web Conference (WWW7) in April 1998. Based on the algorithm, they built the search engine Google. The main ideas of PageRank and HITS are really quite similar. However, it is their dissimilarity that made a huge difference as we will see later. Since that year, PageRank has emerged as the dominant link analysis model for Web search, partly due to its query-independent evaluation of Web pages and its ability to combat spamming, and partly due to Google's business success. In this section, we focus on PageRank.

PageRank relies on the democratic nature of the Webby using its vast link structure as an indicator of an individual page's quality. In essence, PageRank interprets a hyperlink from page x to page y as a vote, by page x, for page y. However, PageRank looks at more than just the sheer number of votes or links that a page receives. It also analyzes the page that casts the vote. Votes cast by pages that are themselves "important" weigh more heavily and help to make other pages more "important." This is exactly the idea of rank prestige in social networks.

Page Rank Algorithm

PageRank is a static ranking of Web pages in the sense that a PageRank value is computed for each page offline and it does not depend on search queries. Since PageRank is based on the measure of prestige in social networks, the PageRank value of each page can be regarded as its prestige. The PageRank computations require several passes, called "iterations", through the collection to adjust approximate PageRank values to more closely reflect the theoretical true value. Thus, the iterative PageRank formula for a given site P. in the next iteration is given as:


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