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RANKING HIGH IN GOOGLE
PageRank
Mathematical PageRanks (out of 100) for a simple network (PageRanks
reported by google are rescaled logarithmically). Page C has a higher
PageRank than Page E, even though it has fewer links to it: the link it has
is much higher valued. A web surfer who chooses a random link on every page
(but with 15% likelihood jumps to a random page on the whole web) is going
to be on Page E for 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. Page A is assumed to link to all pages in the web,
because it has no outgoing links.
Mathematical PageRanks (out of 100) for a simple network (PageRanks reported
by google are rescaled logarithmically). Page C has a higher PageRank than
Page E, even though it has fewer links to it: the link it has is much higher
valued. A web surfer who chooses a random link on every page (but with 15%
likelihood jumps to a random page on the whole web) is going to be on Page E
for 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. Page A is assumed to link to all pages in the web, because it
has no outgoing links.
PageRank is a link analysis algorithm that assigns a numerical weighting to
each element of a hyperlinked set of documents, such as the World Wide Web,
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).
PageRank was developed at Stanford University by Larry Page (hence the name
Page-Rank[1]) and later Sergey Brin as part of a research project about a
new kind of search engine. The project started in 1995 and led to a
functional prototype, named Google, in 1998. Shortly after, Page and Brin
founded Google Inc., the company behind the Google search engine. While just
one of many factors which determine the ranking of Google search results,
PageRank continues to provide the basis for all of Google's web search
tools.
The name PageRank is a trademark of Google. The PageRank process has been
patented (U.S. Patent 6,285,999 ). The patent is not assigned to Google but
to Stanford University.
General 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 your site’s importance in the eyes of
Google. The scale for PageRank is logarithmic like the Richter Scale and
roughly based upon quantity of inbound links as well as importance of the
page providing the link.
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.
Alternatives to the PageRank algorithm include the HITS algorithm proposed
by Jon Kleinberg, the IBM CLEVER project and the TrustRank algorithm.
PageRank algorithm
PageRank is 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 any-size collection of documents. It is
assumed in several research papers that the distribution is evenly divided
between all documents in the collection at the beginning of the
computational process. The PageRank computations require several passes,
called "iterations", through 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 PageRank of 0.5 means there is a 50% chance that a person clicking
on a random link will be directed to the document with the 0.5 PageRank.
Simplified PageRank algorithm
Assume a small universe of four web pages: A, B, C and D. The initial
approximation of PageRank would be evenly divided between these four
documents. Hence, each document would begin with an estimated PageRank of
0.25.
If pages B, C, and D each only link to A, they would each confer 0.25
PageRank to A. All PageRank PR( ) in this simplistic system would thus
gather to A because all links would be pointing to A.
PR(A)= PR(B) + PR(C) + PR(D).\,
But then suppose page B also has a link to page C, and page D has links to
all three pages. The value of the link-votes is divided among all the
outbound links on a page. Thus, page B gives a vote worth 0.125 to page A
and a vote worth 0.125 to page C. Only one third of D's PageRank is counted
for A's PageRank (approximately 0.083).
In other words, the PageRank conferred by an outbound link L( ) is equal to
the document's own PageRank score divided by the normalized number of
outbound links (it is assumed that links to specific URLs only count once
per document).
In the general case, the PageRank value for any page u can be expressed as:
PR(u) = \sum_{v \in B_u} \frac{PR(v)}{L(v)},
i.e. the PageRank value for a page u is dependent on the PageRank values for
each page v out of the set Bu (this set contains all pages linking to page
u), divided by the number L(v) of links from page v.
PageRank algorithm including damping factor
The PageRank theory holds that even an imaginary surfer who is randomly
clicking on links will eventually stop clicking. The probability, at any
step, that the person will continue is a damping factor 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 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,
PR(A)= 1 - d + d \left( \frac{PR(B)}{L(B)}+ \frac{PR(C)}{L(C)}+ \frac{PR(D)}{L(D)}+\,\cdots
\right)
or (N = the number of documents in collection)
PR(A)= {1 - d \over N} + d \left( \frac{PR(B)}{L(B)}+ \frac{PR(C)}{L(C)}+ \frac{PR(D)}{L(D)}+\,\cdots
\right) .
So any page's PageRank is derived in large part from the PageRanks of other
pages. The damping factor adjusts the derived value downward. The second
formula above supports the original statement in Page and Brin's paper that
"the sum of all PageRanks is one". Unfortunately, however, Page and Brin
gave the first formula, which has led to some confusion.
Google recalculates PageRank scores each time it crawls the Web and rebuilds
its index. As Google increases 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 gets bored after several
clicks and 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 the states are pages,
and the transitions are all equally probable and are the links between
pages.
If a page has no links to other pages, it becomes a sink and therefore
terminates the random surfing process. However, the solution is quite
simple. 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 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, with a residual probability of usually d = 0.85, estimated
from the frequency that an average surfer uses his or her browser's bookmark
feature.
So, the equation is as follows:
PR(p_i) = \frac{1-d}{N} + d \sum_{p_j \in M(p_i)} \frac{PR (p_j)}{L(p_j)}
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.
The PageRank values are the entries of the dominant eigenvector of the
modified adjacency matrix. This makes PageRank a particularly elegant
metric: the eigenvector is
where the adjacency function \ell(p_i,p_j) is 0 if page pj does not link to
pi, and normalised such that, for each j
\sum_{i = 1}^N \ell(p_i,p_j) = 1,
i.e. the elements of each column sum up to 1.
This is a variant of the eigenvector centrality measure used commonly in
network analysis.
The values of the PageRank eigenvector are fast to approximate (only a few
iterations are needed) and in practice it gives good results.
As a result of Markov theory, it can be shown that the PageRank of a page is
the probability of being at that page after lots of clicks. This happens to
equal t − 1 where t is the expectation of the number of clicks (or random
jumps) required to get from the page back to itself.
The main disadvantage is that it favors older pages, because 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
Wikipedia). The Google Directory (itself a derivative of the Open Directory
Project) allows users to see results sorted by PageRank within categories.
The Google Directory is the only service offered by Google where PageRank
directly determines display order. In Google's other search services (such
as its primary Web search) PageRank is used to weight the relevance scores
of pages shown in search results.
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 the reliability of the PageRank
concept, which seeks to determine which documents are actually highly valued
by the Web community.
Google is known to actively penalize link farms and other schemes designed
to artificially inflate PageRank. How Google identifies link farms and other
PageRank manipulation tools are among Google's trade secrets.
PageRank variations
Google Toolbar
An example of the PageRank indicator as found on the Google toolbar.
An example of the PageRank indicator as found on the Google toolbar.
The Google Toolbar's PageRank feature displays a visited page's PageRank as
a whole number between 0 and 10. The most popular websites have a PageRank
of 10. The least have a PageRank of 0. Google has not disclosed the precise
method for determining a Toolbar PageRank value. Google representative Matt
Cutts has publicly indicated that the Toolbar PageRank values are
republished about once every three months, indicating that the Toolbar
PageRank values are historical rather than real-time values.
Google directory PageRank
The Google Directory PageRank is an 8-unit measurement. These values can be
viewed in the Google Directory. Unlike the Google Toolbar which shows the
PageRank value by a mouseover of the greenbar, the Google Directory does not
show the PageRank as a numeric value but only as a green bar.
False or spoofed PageRank
While the PR shown in the Toolbar is considered to be derived from an
accurate PageRank value (at some time prior to the time of publication by
Google) for most sites, it must be noted that this value is also easily
manipulated. A current flaw is that any low PageRank page that is
redirected, via a 302 server header or a "Refresh" meta tag, to a high PR
page causes the lower PR page to acquire the PR of the destination page. In
theory a new, PR0 page with no incoming links can be redirected to the
Google home page - which is a PR 10 - and by the next PageRank update the PR
of the new page will be upgraded to a PR10. This spoofing technique, also
known as 302 Google Jacking, is a known failing or bug in the system. Any
page's PR can be spoofed to a higher or lower number of the webmaster's
choice and only Google has access to the real PR of the page. Spoofing is
generally detected by running a Google search for a URL with questionable
PR, as the results will display the URL of an entirely different site (the
one redirected to) in its results.
Manipulating PageRank
For search-engine optimization purposes, some companies offer to sell high
PageRank links to webmasters. As links from higher-PR pages are believed to
be more valuable, they tend to be more expensive. It can be an effective and
viable marketing strategy to buy link advertisements on content pages of
quality and relevant sites to drive traffic and increase a webmaster's link
popularity. However, Google has publicly warned webmasters that if they are
or were discovered to be selling links for the purpose of conferring
PageRank and reputation, their links will be devalued (ignored in the
calculation of other pages' PageRanks). The practice of buying and selling
links is intensely debated across the Webmastering community. Google advises
webmasters to use the nofollow HTML attribute value on sponsored links.
According to Matt Cutts, Google is concerned about webmasters who try to
game the system, and thereby reduce the quality of Google search results.
Other uses of PageRank
A version of PageRank has recently been proposed as a replacement for the
traditional ISI impact factor,[8] and implemented at eigenfactor.org.
Instead of merely counting total citation to a journal, the "importance" of
each citation is determined in a PageRank fashion.
A similar new use of PageRank is to rank academic doctoral programs based on
their records of placing their graduates in faculty positions. In PageRank
terms, academic departments link to each other by hiring their faculty from
each other (and from themselves).
PageRank has also been used to automatically rank WordNet synsets according
to how strongly they possess a given semantic property, such as positivity
or negativity.
A dynamic weighting method similar to PageRank has been used to generate
customized reading lists based on the link structure of Wikipedia.
A Web crawler may use PageRank as one of a number of importance metrics it
uses to determine which URL to visit next during a crawl of the web. One of
the early working papers which were used in the creation of Google is
Efficient crawling through URL ordering, which discusses the use of a number
of different importance metrics to determine how deeply, and how much of a
site Google will crawl. PageRank is presented as one of a number of these
importance metrics, though there are others listed such as the number of
inbound and outbound links for a URL, and the distance from the root
directory on a site to the URL.
Google's "rel='nofollow'" proposal
In early 2005, Google implemented a new value, "nofollow", for the rel
attribute of HTML link and anchor elements, so that website builders and
bloggers can make links that Google will not consider for the purposes of
PageRank — they are links that no longer constitute a "vote" in the PageRank
system. The nofollow relationship was added in an attempt to help combat
spamdexing.
As an example, people could create many message-board posts with links to
their website to artificially inflate their PageRank. Now, however, the
message-board administrator can modify the code to automatically insert "rel='nofollow'"
to all hyperlinks in posts, thus preventing PageRank from being affected by
those particular posts.
This method of avoidance, however, also has various drawbacks, such as
reducing the link value of actual comments.
From Wikipedia, the free encyclopedia
http://en.wikipedia.org/wiki/Page_Rank