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• Title: PageRank

# PageRank

페이지랭크(PageRank)는 월드 와이드 웹과 같은 하이퍼링크 구조를 가지는 문서에 상대적 중요도에 따라 가중치를 부여하는 방법이다. 이 알고리즘은 서로간에 인용과 참조로 연결된 임의의 묶음에 적용할 수 있다.

페이지랭크는 스탠퍼드 대학교에 재학 중이던 래리 페이지와 세르게이 브린이 새로운 검색 엔진에 대한 연구 기획의 일부로 개발한 것이다. 이 기획은 1995년 시작되어, 1998년 구글이라 불리는 시범 서비스로 발전하였다. 페이지와 브린은 페이지랭크에 기반한 검색 기술을 바탕으로 구글 사를 설립하였다.

Mathematical PageRanks for a simple network, expressed as percentages. (Google uses a logarithmic scale.) Page C has a higher PageRank than Page E, even though there are fewer links to C; the one link to C comes from an important page and hence is of high value. If web surfers who start on a random page have an 85% likelihood of choosing a random link from the page they are currently visiting, and a 15% likelihood of jumping to a page chosen at random from the entire web, they will reach Page E 8.1% of the time. (The 15% likelihood of jumping to an arbitrary page corresponds to a damping factor of 85%.) Without damping, all web surfers would eventually end up on Pages A, B, or C, and all other pages would have PageRank zero. In the presence of damping, Page A effectively links to all pages in the web, even though it has no outgoing links of its own.

PageRank is an algorithm used by Google Search to rank websites in their search engine results. PageRank was named after Larry Page,[1] one of the founders of Google. 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.

It is not the only algorithm used by Google to order search engine results, but it is the first algorithm that was used by the company, and it is the best-known.[2][3] Google uses an automated web spider called Googlebot to actually count links and gather other information on web pages.

페이지 랭크는 더 중요한 페이지는 더 많은 다른 사이트로부터 링크를 받는다는 관찰에 기초하고 있다. 예를들어 페이지 A가 페이지 B,C,D 로 총 3개의 링크를 걸었다면 B는 A의 페이지 랭크 값의 $1 \over 3$ 만큼을 가져온다.

또한 페이지 랭크에서는 랜덤 서퍼(Random Sufer)라는 페이지를 임의로 방문하며 탐색하는 모델을 가정한다. 이 모델에서는 위 예의 A페이지를 방문한 서퍼는 A페이지를 보고 만족하여 탐색을 중단하거나, 혹은 A페이지에서 만족하지 못하여 다른 페이지를 방문할 것이다. 이러한 확률(Damping Factor)을 $\alpha$라 한다면, B페이지는 $\alpha * {1 \over 3}$만큼 페이지 랭크를 받게 된다.

페이지 랭크는 이와 같은 방법을 통해 페이지간 페이지 랭크 값을 주고 받는 것을 반복하다보면, 전체 웹 페이지가 특정한 페이지 랭크 값을 수렴한다는 사실을 통해 각 페이지의 최종 페이지 랭크를 계산한다.

The name “PageRank” plays off of the name of developer Larry Page, as well as the concept of a web page.[11] 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; the shares were sold in 2005 for $336 million.[12][13] PageRank 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 important work on HITS. Google’s founders cite Garfield, Marchiori, and Kleinberg in their original papers.[4][14] A small search engine called “RankDex” from IDD Information Services designed by Robin Li was, since 1996, already exploring a similar strategy for site-scoring and page ranking.[15] The technology in RankDex would be patented by 1999[16] and used later when Li founded Baidu in China.[17][18] Li’s work would be referenced by some of Larry Page’s U.S. patents for his Google search methods.[19] # References 1. “Google Press Center: Fun Facts”. www.google.com. Archived from the original on 2009-04-24. 2. http://www.google.com/competition/howgooglesearchworks.html. Missing or empty (help) 3. Sullivan, Danny. “What Is Google PageRank? A Guide For Searchers & Webmasters”. Search Engine Land. 4. Brin, S.; Page, L. (1998). “The anatomy of a large-scale hypertextual Web search engine”. Computer Networks and ISDN Systems 30: 107–117. doi:10.1016/S0169-7552(98)00110-X. ISSN 0169-7552edit 5. Gyöngyi, Zoltán; Berkhin, Pavel; Garcia-Molina, Hector; Pedersen, Jan (2006), “Link spam detection based on mass estimation”, Proceedings of the 32nd International Conference on Very Large Data Bases (VLDB ‘06, Seoul, Korea), pp. 439–450 . 6. Gabriel Pinski and Francis Narin. “Citation influence for journal aggregates of scientific publications: Theory, with application to the literature of physics”. Information Processing & Management 12 (5): 297–312. doi:10.1016/0306-4573(76)90048-0 7. Raphael Phan Chung Wei (2002-05-16). “Resources”. New Straits Times (Computimes; 2 ed.). 8. Page, Larry, “PageRank: Bringing Order to the Web” at the Wayback Machine (archived May 6, 2002), Stanford Digital Library Project, talk. August 18, 1997 (archived 2002) 9. 187-page study from Graz University, Austria, includes the note that also human brains are used when determining the page rank in Google 10. “Google Technology”. Google.com. Retrieved 2011-05-27. 11. David Vise and Mark Malseed (2005). The Google Story. p. 37. ISBN 0-553-80457-X 12. Lisa M. Krieger (1 December 2005). “Stanford Earns$336 Million Off Google Stock”. San Jose Mercury News, cited by redOrbit. Retrieved 2009-02-25.
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