What is Page Rank
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Description
Google describes PageRank:
“ PageRank relies on the uniquely democratic nature of the web by using
its vast link structure as an indicator of an individual page's value. In
essence, Google interprets a link from page A to page B as a vote, by page
A, for page B. But, Google looks at more than the sheer volume of votes,
or links 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 "important". ”
In other words, a PageRank results from a "ballot" among all the other
pages on the World Wide Web about how important a page is. 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 is linked to by many pages
with high PageRank receives a high rank itself. If there are no links to a
web page there is no support for that page.
Google assigns a numeric weighting from 0-10 for each webpage on the
Internet; this PageRank denotes a site’s importance in the eyes of Google.
The PageRank is derived from a theoretical probability value on a
logarithmic scale like the Richter Scale. The PageRank of a particular page
is roughly based upon the quantity of inbound links as well as the PageRank
of the pages providing the links. It is known that other factors, e.g.
relevance of search words on the page and actual visits to the page
reported by the Google toolbar also influence the PageRank. In order to
prevent manipulation, spoofing and spamdexing, Google provides no
specific details about how other factors influence PageRank.
Numerous academic papers concerning PageRank have been published
since Page and Brin's original paper. In practice, the PageRank concept has
proven to be vulnerable to manipulation, and extensive research has been
devoted to identifying falsely inflated PageRank and ways to ignore links
from documents with falsely inflated PageRank.
Other link-based ranking algorithms for Web pages include the HITS
algorithm invented by Jon Kleinberg (used by Teoma and now Ask.com
Search Engine - Better Web Search), the IBM CLEVER project, and the
TrustRank algorithm.
Google describes PageRank:
“ PageRank relies on the uniquely democratic nature of the web by using
its vast link structure as an indicator of an individual page's value. In
essence, Google interprets a link from page A to page B as a vote, by page
A, for page B. But, Google looks at more than the sheer volume of votes,
or links 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 "important". ”
In other words, a PageRank results from a "ballot" among all the other
pages on the World Wide Web about how important a page is. 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 is linked to by many pages
with high PageRank receives a high rank itself. If there are no links to a
web page there is no support for that page.
Google assigns a numeric weighting from 0-10 for each webpage on the
Internet; this PageRank denotes a site’s importance in the eyes of Google.
The PageRank is derived from a theoretical probability value on a
logarithmic scale like the Richter Scale. The PageRank of a particular page
is roughly based upon the quantity of inbound links as well as the PageRank
of the pages providing the links. It is known that other factors, e.g.
relevance of search words on the page and actual visits to the page
reported by the Google toolbar also influence the PageRank. In order to
prevent manipulation, spoofing and spamdexing, Google provides no
specific details about how other factors influence PageRank.
Numerous academic papers concerning PageRank have been published
since Page and Brin's original paper. In practice, the PageRank concept has
proven to be vulnerable to manipulation, and extensive research has been
devoted to identifying falsely inflated PageRank and ways to ignore links
from documents with falsely inflated PageRank.
Other link-based ranking algorithms for Web pages include the HITS
algorithm invented by Jon Kleinberg (used by Teoma and now Ask.com
Search Engine - Better Web Search), the IBM CLEVER project, and the
TrustRank algorithm.
Page rank is as follows:
where p1,p2,...,pN are the pages under consideration, M(pi) is the set of pages that link to pi, L(pj) is the number of outbound links on page pj, and N is the total number of pages.
nice post sir.....
I think its hard to compute the PR of the site
PR is measured of a site rank that gives by google search engine.
PR is measured of a site rank that gives by google search engine.
Page Rank is refer as a numeric value that represents the value of a sites which range PR0-PR10. It measures the popularity of your website based on back links and quality of the sites providing the links. If your site is at a higher PR, then lucky you are because you got a better and quality site.
hello...
ya if we are working for internet marketing..then 1thing we have to keep in our mind that PPC is the most important part.we can know the status of a website throgh their page rank..juzz like our report card at school...
ya if we are working for internet marketing..then 1thing we have to keep in our mind that PPC is the most important part.we can know the status of a website throgh their page rank..juzz like our report card at school...
PageRank is a link analysis algorithm, named after Larry Page,used by the Google Internet search engine that assigns a numerical weighting to each element of a hyperlinked set of documents, with 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 also called the PageRank of E and denoted by PR(E)
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