Page rank algorithm depends on various factors that Google knows. Some main factors that effect page rank are domain age, backlinks, domain authority, bounce rate and CTR.
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Page rank algorithm depends on various factors that Google knows. Some main factors that effect page rank are domain age, backlinks, domain authority, bounce rate and CTR.
hey thanks for this but can tell for what it is used...
Page rank algorithm which is used to consider your site web pages in their search engine listings. Page rank uses a link-based algorithm that is dependent on your site incoming links and content.
From this Page Rank Algorithm we increase the Page Rank of our website pages.
Page Rank is a denotes as a numerical method it is increase by the backlinks of website.
The original PageRank algorithm was described by Lawrence Page and Sergey Brin in several publications. It is given by
PR(A) = (1-d) + d (PR(T1)/C(T1) + ... + PR(Tn)/C(Tn))
where
PR(A) is the PageRank of page A,
PR(Ti) is the PageRank of pages Ti which link to page A,
C(Ti) is the number of outbound links on page Ti and
d is a damping factor which can be set between 0 and 1.
So, first of all, we see that PageRank does not rank web sites as a whole, but is determined for each page individually. Further, the PageRank of page A is recursively defined by the PageRanks of those pages which link to page A.
The original PageRank algorithm was described by Lawrence Page and Sergey Brin in several publications. It is given by
PR(A) = (1-d) + d (PR(T1)/C(T1) + ... + PR(Tn)/C(Tn))
where
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PR(A) is the PageRank of page A, |
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PR(Ti) is the PageRank of pages Ti which link to page A, |
![]() |
C(Ti) is the number of outbound links on page Ti and |
![]() |
d is a damping factor which can be set between 0 and 1. |
PageRank is the algorithm used by the Google search engine, originally formulated by Sergey Brin and Larry Page in their paper The Anatomy of a Large-Scale Hypertextual Web Search Engine.
It is based on the premise, prevalent in the world of academia, that the importance of a research paper can be judged by the number of citations the paper has from other research papers. Brin and Page have simply transferred this premise to its web equivalent: the importance of a web page can be judged by the number of hyperlinks pointing to it from other web pages
It's no secret anymore that Google ranks as the number one defacto-standard in the field of major search engines. Today, Google accounts for more than 85 percent of all Internet searches on a daily basis. Google now has many versions running in many different countries, including China, Japan, the U.K., Hong-Kong and many others. Rank for Sales knows of many small and not so small businesses whose livelihood almost basically depends on Google bringing them new customers, day after day.
When the livelihood of an entire company depends on just one search engine, this tells you a lot about the success of Google. It also underlines the importance of any web site ranking high in Google. In order to develop an independant and objective ranking system that has integrity, is both fair to everyone and is efficient for all end users searching on a specific keyword or keyphrase, Google has developed the Page Rank (PR) Algorithm.
CALCULATION OF PAGERANK The PageRank of a page A is given as follows: PR(A) = (1-d) + d (PR(T1)/C(T1) + … + PR(Tn)/C(Tn)) A T1 T2 T3 . . . . . . . . TN PR(T1) PR(T2) PR(T3) . . . . . . . . PR(TN) C(A)
PR(A) = (1-d) + d (PR(T1)/C(T1) + … + PR(Tn)/C(Tn)) The PR of each page depends on the PR of the pages pointing to it. We won’t know what PR those pages have until the pages pointing to them have their PR calculated