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44-971: (Redirected from Pr ) [REDACTED] Look up PR  or .pr in Wiktionary, the free dictionary. PR , P.R. , Pr , pr , or Pr. may refer to: Arts, entertainment, and media [ edit ] P.R. (TV series) , a Canadian television sitcom Partisan Review , a former political and literary journal Perry Rhodan , German science fiction series Power Rangers , an American television franchise based on Super Sentai Polskie Radio , Poland's national radio broadcasting organization Places [ edit ] PR postcode area , UK, including Preston and Lancashire Paraná (state) , Brazil (ISO 3166-2:BR) Parma , Italy (ISO 3166-2:IT) Puerto Rico , ISO 3166 code PR Politics [ edit ] Pakatan Rakyat , an informal Malaysian political coalition Party of Labour ( Partija rada ),

88-452: A probability distribution used to represent the likelihood that a person randomly clicking on links will arrive at any particular page. PageRank can be calculated for collections of documents of any size. It is assumed in several research papers that the distribution is evenly divided among all documents in the collection at the beginning of the computational process. The PageRank computations require several passes, called "iterations", through

132-489: A stochastic matrix (for more details see the computation section below). Thus this is a variant of the eigenvector centrality measure used commonly in network analysis . Because of the large eigengap of the modified adjacency matrix above, the values of the PageRank eigenvector can be approximated to within a high degree of accuracy within only a few iterations. Google's founders, in their original paper, reported that

176-604: A chemical element Prandtl number , in physics and engineering, typically denoted Pr Propyl radicals or groups, denoted Pr in organic chemistry Sports [ edit ] Personal record , an individual's best result in a particular event Pinch runner , in baseball Protected ranking , a method of ranking tennis players coming back from injury Punt returner , a position in American football Transportation [ edit ] Pakistan Railways (reporting mark PR) Polregio (formerly Przewozy Regionalne),

220-420: A citation in some of his U.S. patents for PageRank. Larry Page and Sergey Brin developed PageRank at Stanford University in 1996 as part of a research project about a new kind of search engine. An interview with Héctor García-Molina , Stanford Computer Science professor and advisor to Sergey, provides background into the development of the page-rank algorithm. Sergey Brin had the idea that information on

264-540: A measurement used in cardiology PR-104 Progesterone receptor , a protein Computing [ edit ] PR (complexity) , a complexity class pr , Unix command to paginate or columnate files for printing Adobe Premiere Pro , software which uses "Pr" as its icon abbreviation Pagerank , a Google technology Performance Rating , a computing term by AMD Project Reality , series of video games Pull request , part of distributed version control Part of

308-639: A polish railway operator Park and ride , a type of car park with public transport connections Philippine Airlines (IATA airline designation PR) Poste restante , mail held for collection Other uses [ edit ] Pakistan Rangers , a paramilitary force of Pakistan Parachute rigger , a former U.S. Navy rank Permanent residency Princess Royal (disambiguation) Professor A US Navy hull classification symbol: Patrol river gunboat (PR) PR , in linguistics, glossing abbreviation for present tense See also [ edit ] P&R (disambiguation) Topics referred to by

352-570: A political party in Serbia Proportional representation , a property of some voting systems Radical Party of Chile ( Partido Radical ), 1863–1994 Radical Party of Chile (2018) ( Partido Radical de Chile ) Republican Party of Albania , a political party in Albania Public relations [ edit ] Public relations , the professional maintenance of a favorable public image by an organisation or person Press release ,

396-733: A prepared statement given to the news media as a public-relations tool Religion [ edit ] Pastor , an ordained leader of a Christian congregation Permanent rector , or permanens rector , of a parish Science, technology, and mathematics [ edit ] Biology and medicine [ edit ] Partial Response, a component of Response Evaluation Criteria in Solid Tumors Pathogenesis-related proteins, produced by plants under pathogen attack Penicillium roqueforti Per rectum , meaning "administered rectally" Pigment red (e.g. Pigment Red 179 , Pigment Red 190 ) or para red PR interval ,

440-469: A small universe of four web pages: A , B , C , and D . Links from a page to itself are ignored. Multiple outbound links from one page to another page are treated as a single link. PageRank is initialized to the same value for all pages. In the original form of PageRank, the sum of PageRank over all pages was the total number of pages on the web at that time, so each page in this example would have an initial value of 1. However, later versions of PageRank, and

484-599: A strategy for site-scoring and page-ranking. Li referred to his search mechanism as "link analysis," which involved ranking the popularity of a web site based on how many other sites had linked to it. RankDex, the first search engine with page-ranking and site-scoring algorithms, was launched in 1996. Li filed a patent for the technology in RankDex in 1997; it was granted in 1999. He later used it when he founded Baidu in China in 2000. Google founder Larry Page referenced Li's work as

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528-446: Is 1 - d . Various studies have tested different damping factors, but it is generally assumed that the damping factor will be set around 0.85. The damping factor is subtracted from 1 (and in some variations of the algorithm, the result is divided by the number of documents ( N ) in the collection) and this term is then added to the product of the damping factor and the sum of the incoming PageRank scores. That is, So any page's PageRank

572-550: Is derived in large part from the PageRanks of other pages. The damping factor adjusts the derived value downward. The original paper, however, gave the following formula, which has led to some confusion: The difference between them is that the PageRank values in the first formula sum to one, while in the second formula each PageRank is multiplied by N and the sum becomes N . A statement in Page and Brin's paper that "the sum of all PageRanks

616-498: Is different from Wikidata All article disambiguation pages All disambiguation pages PR">PR The requested page title contains unsupported characters : ">". Return to Main Page . Pagerank 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. Currently, PageRank

660-499: Is linked to by many pages with high PageRank receives a high rank itself. Numerous academic papers concerning PageRank have been published since Page and Brin's original paper. In practice, the PageRank concept may be vulnerable to manipulation. Research has been conducted into identifying falsely influenced PageRank rankings. The goal is to find an effective means of ignoring links from documents with falsely influenced PageRank. Other link-based ranking algorithms for Web pages include

704-458: Is not the only algorithm used by Google to order search results, but it is the first algorithm that was used by the company, and it is the best known. As of September 24, 2019, all patents associated with PageRank have expired. PageRank is a link analysis algorithm and it assigns a numerical weighting to each element of a hyperlinked set of documents, such as the World Wide Web , with

748-488: Is one" and claims by other Google employees support the first variant of the formula above. Page and Brin confused the two formulas in their most popular paper "The Anatomy of a Large-Scale Hypertextual Web Search Engine", where they mistakenly claimed that the latter formula formed a probability distribution over web pages. Google recalculates PageRank scores each time it crawls the Web and rebuilds its index. As Google increases

792-451: Is the damping factor, or in matrix notation where R i ( t ) = P R ( p i ; t ) {\displaystyle \mathbf {R} _{i}(t)=PR(p_{i};t)} and 1 {\displaystyle \mathbf {1} } is the column vector of length N {\displaystyle N} containing only ones. The matrix M {\displaystyle {\mathcal {M}}}

836-400: Is the ratio between number of links outbound from page j to page i to the total number of outbound links of page j. The adjacency function is 0 if page p j {\displaystyle p_{j}} does not link to p i {\displaystyle p_{i}} , and normalized such that, for each j i.e. the elements of each column sum up to 1, so the matrix is

880-416: Is the size of the network. As a result of Markov theory , it can be shown that the PageRank of a page is the probability of arriving at that page after a large number of clicks. This happens to equal t − 1 {\displaystyle t^{-1}} where t {\displaystyle t} is the expectation of the number of clicks (or random jumps) required to get from

924-442: Is the total number of pages. The PageRank values are the entries of the dominant right eigenvector of the modified adjacency matrix rescaled so that each column adds up to one. This makes PageRank a particularly elegant metric: the eigenvector is where R is the solution of the equation where the adjacency function ℓ ( p i , p j ) {\displaystyle \ell (p_{i},p_{j})}

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968-688: The HITS algorithm invented by Jon Kleinberg (used by Teoma and now Ask.com ), the IBM CLEVER project , the TrustRank algorithm, the Hummingbird algorithm, and the SALSA algorithm . The eigenvalue problem behind PageRank's algorithm was independently rediscovered and reused in many scoring problems. In 1895, Edmund Landau suggested using it for determining the winner of a chess tournament. The eigenvalue problem

1012-485: The YPbPr standard Mathematics [ edit ] PR (complexity) , a complexity class Pr( E ), also P( E ), the probability of an event E pr i , a notation for the scalar projection onto the i -th component Positive-real function , in mathematics Proportional representation , a property of some voting systems Other uses in science and technology [ edit ] Praseodymium , symbol Pr,

1056-445: The power iteration method or the power method. The basic mathematical operations performed are identical. At t = 0 {\displaystyle t=0} , an initial probability distribution is assumed, usually where N is the total number of pages, and p i ; 0 {\displaystyle p_{i};0} is page i at time 0. At each time step, the computation, as detailed above, yields where d

1100-436: The webgraph , created by all World Wide Web pages as nodes and hyperlinks as edges, taking into consideration authority hubs such as cnn.com or mayoclinic.org . The rank value indicates an importance of a particular page. A hyperlink to a page counts as a vote of support. The PageRank of a page is defined recursively and depends on the number and PageRank metric of all pages that link to it (" incoming links "). A page that

1144-472: The PageRank algorithm for a network consisting of 322 million links (in-edges and out-edges) converges to within a tolerable limit in 52 iterations. The convergence in a network of half the above size took approximately 45 iterations. Through this data, they concluded the algorithm can be scaled very well and that the scaling factor for extremely large networks would be roughly linear in log ⁡ n {\displaystyle \log n} , where n

1188-404: The collection to adjust approximate PageRank values to more closely reflect the theoretical true value. A probability is expressed as a numeric value between 0 and 1. A 0.5 probability is commonly expressed as a "50% chance" of something happening. Hence, a document with a PageRank of 0.5 means there is a 50% chance that a person clicking on a random link will be directed to said document. Assume

1232-549: The collection. Their PageRank scores are therefore divided evenly among all other pages. In other words, to be fair with pages that are not sinks, these random transitions are added to all nodes in the Web. This residual probability, d , is usually set to 0.85, estimated from the frequency that an average surfer uses his or her browser's bookmark feature. So, the equation is as follows: where p 1 , p 2 , . . . , p N {\displaystyle p_{1},p_{2},...,p_{N}} are

1276-430: The completion of this iteration, page A will have a PageRank of approximately 0.458. In other words, the PageRank conferred by an outbound link is equal to the document's own PageRank score divided by the number of outbound links L( ) . In the general case, the PageRank value for any page u can be expressed as: i.e. the PageRank value for a page u is dependent on the PageRank values for each page v contained in

1320-441: The concept of a web page . The word is a trademark of Google, and the PageRank process has been patented ( U.S. patent 6,285,999 ). However, the patent is assigned to Stanford University and not to Google. Google has exclusive license rights on the patent from Stanford University. The university received 1.8 million shares of Google in exchange for use of the patent; it sold the shares in 2005 for US$ 336 million. PageRank

1364-508: The initial prototype of the Google search engine , published in 1998. Shortly after, Page and Brin founded Google Inc. , the company behind the Google search engine. While just one of many factors that determine the ranking of Google search results, PageRank continues to provide the basis for all of Google's web-search tools. The name "PageRank" plays on the name of developer Larry Page, as well as of

PR - Misplaced Pages Continue

1408-538: The next iteration, for a total of 0.75. Suppose instead that page B had a link to pages C and A , page C had a link to page A , and page D had links to all three pages. Thus, upon the first iteration, page B would transfer half of its existing value (0.125) to page A and the other half (0.125) to page C . Page C would transfer all of its existing value (0.25) to the only page it links to, A . Since D had three outbound links, it would transfer one third of its existing value, or approximately 0.083, to A . At

1452-409: The number of documents in its collection, the initial approximation of PageRank decreases for all documents. The formula uses a model of a random surfer who reaches their target site after several clicks, then switches to a random page. The PageRank value of a page reflects the chance that the random surfer will land on that page by clicking on a link. It can be understood as a Markov chain in which

1496-533: The page back to itself. One main disadvantage of PageRank is that it favors older pages. A new page, even a very good one, will not have many links unless it is part of an existing site (a site being a densely connected set of pages, such as Misplaced Pages ). Several strategies have been proposed to accelerate the computation of PageRank. Various strategies to manipulate PageRank have been employed in concerted efforts to improve search results rankings and monetize advertising links. These strategies have severely impacted

1540-435: The pages under consideration, M ( p i ) {\displaystyle M(p_{i})} is the set of pages that link to p i {\displaystyle p_{i}} , L ( p j ) {\displaystyle L(p_{j})} is the number of outbound links on page p j {\displaystyle p_{j}} , and N {\displaystyle N}

1584-473: The purpose of "measuring" its relative importance within the set. The algorithm may be applied to any collection of entities with reciprocal quotations and references. The numerical weight that it assigns to any given element E is referred to as the PageRank of E and denoted by P R ( E ) . {\displaystyle PR(E).} A PageRank results from a mathematical algorithm based on

1628-529: The reliability of the PageRank concept, which purports to determine which documents are actually highly valued by the Web community. Since December 2007, when it started actively penalizing sites selling paid text links, Google has combatted link farms and other schemes designed to artificially inflate PageRank. How Google identifies link farms and other PageRank manipulation tools is among Google's trade secrets . PageRank can be computed either iteratively or algebraically. The iterative method can be viewed as

1672-424: The remainder of this section, assume a probability distribution between 0 and 1. Hence the initial value for each page in this example is 0.25. The PageRank transferred from a given page to the targets of its outbound links upon the next iteration is divided equally among all outbound links. If the only links in the system were from pages B , C , and D to A , each link would transfer 0.25 PageRank to A upon

1716-401: The same term [REDACTED] This disambiguation page lists articles associated with the title PR . If an internal link led you here, you may wish to change the link to point directly to the intended article. Retrieved from " https://en.wikipedia.org/w/index.php?title=PR&oldid=1258643655 " Category : Disambiguation pages Hidden categories: Short description

1760-400: The set B u (the set containing all pages linking to page u ), divided by the number L ( v ) of links from page v . The PageRank theory holds that an imaginary surfer who is randomly clicking on links will eventually stop clicking. The probability, at any step, that the person will continue following links is a damping factor d . The probability that they instead jump to any random page

1804-433: The states are pages, and the transitions are the links between pages – all of which are all equally probable. If a page has no links to other pages, it becomes a sink and therefore terminates the random surfing process. If the random surfer arrives at a sink page, it picks another URL at random and continues surfing again. When calculating PageRank, pages with no outbound links are assumed to link out to all other pages in

PR - Misplaced Pages Continue

1848-407: The web could be ordered in a hierarchy by "link popularity": a page ranks higher as there are more links to it. The system was developed with the help of Scott Hassan and Alan Steremberg, both of whom were cited by Page and Brin as being critical to the development of Google. Rajeev Motwani and Terry Winograd co-authored with Page and Brin the first paper about the project, describing PageRank and

1892-500: Was also suggested in 1976 by Gabriel Pinski and Francis Narin, who worked on scientometrics ranking scientific journals, in 1977 by Thomas Saaty in his concept of Analytic Hierarchy Process which weighted alternative choices, and in 1995 by Bradley Love and Steven Sloman as a cognitive model for concepts, the centrality algorithm. A search engine called " RankDex " from IDD Information Services, designed by Robin Li in 1996, developed

1936-528: Was influenced by citation analysis , early developed by Eugene Garfield in the 1950s at the University of Pennsylvania, and by Hyper Search , developed by Massimo Marchiori at the University of Padua . In the same year PageRank was introduced (1998), Jon Kleinberg published his work on HITS . Google's founders cite Garfield, Marchiori, and Kleinberg in their original papers. The PageRank algorithm outputs

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