PageRank

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, 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 referred to as the PageRank of E and denoted by $PR(E).$

The name "PageRank" is a trademark of Google, and the PageRank process has been patented ( ). 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.

Description

Google describes PageRank: . There is no simple way to tell from the rank, if the site is penalized or simply unpopular. Sites are usually penalized by "zero ranking" just for suspected attempts to influence the search engine. Fraud or intentional malware sites are completely removed from the search instead.

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), the IBM CLEVER project, and the TrustRank algorithm.

History

PageRank was developed at Stanford University by Larry Page (hence the name Page-Rank ) and later Sergey Brin as part of a research project about a new kind of search engine. It was co-authored by Rajeev Motwani and Terry Winograd. The first paper about the project, describing PageRank and the initial prototype of the Google search engine, was published in 1998

PageRank has been 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 paper. The technology in RankDex would be patented by 1999 and used later when Li founded Baidu in China. Li's work would be referenced by some of Larry Page's US patents for his Google search methods.

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 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 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 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.

In the original form of PageRank initial values were simply 1. This meant that the sum of all pages was the total number of pages on the web. Later versions of PageRank (see the formulas below) would assume a probability distribution between 0 and 1. Here a simple probability distribution will be used- hence the initial value 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.

: $PR(A)= PR(B) + PR(C) + PR(D).\,$ This is 0.75.

Suppose that page B has a link to page C as well as to page A, while 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).

: $PR(A)= \frac{PR(B)}{2}+ \frac{PR(C)}{1}+ \frac{PR(D)}{3}.\,$

In other words, the PageRank conferred by an outbound link is equal to the document's own PageRank score divided by the normalized number of outbound links L( ) (it is assumed that links to specific URLs only count once per document).

: $PR(A)= \frac{PR(B)}{L(B)}+ \frac{PR(C)}{L(C)}+ \frac{PR(D)}{L(D)}. \,$

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 B_u (this set contains all pages linking to page u), divided by the number L(v) of links from page v.

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. support the first variant of the formula above.

To be more specific, in the latter formula, the probability for the random surfer reaching a page is weighted by the total number of web pages. So, in this version PageRank is an expected value for the random surfer visiting a page, when he restarts this procedure as often as the web has pages. If the web had 100 pages and a page had a PageRank value of 2, the random surfer would reach that page in an average twice if he restarts 100 times. Basically, the two formulas do not differ fundamentally from each other. A PageRank which has been calculated by using the former formula has to be multiplied by the total number of web pages to get the according PageRank that would have been calculated by using the latter formula. Even Page and Brin mixed up the two formulas in their most popular paper "The Anatomy of a Large-Scale Hypertextual Web Search Engine", where they claim the latter formula to form a probability distribution over web pages with the sum of all pages' PageRanks being one.

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. 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 $p_1, p_2, ..., p_N$ are the pages under consideration, $M(p_i)$ is the set of pages that link to $p_i$ , $L(p_j)$ is the number of outbound links on page $p_j$ , 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

: $\mathbf{R} =
\begin{bmatrix}
PR(p_1) \\
PR(p_2) \\
\vdots \\
PR(p_N)
\end{bmatrix}$ where R is the solution of the equation : $\mathbf{R} =

\begin{bmatrix}
{(1-d)/ N} \\
{(1-d) / N} \\
\vdots \\
{(1-d) / N}
\end{bmatrix}

+ d

\begin{bmatrix}
\ell(p_1,p_1) & \ell(p_1,p_2) & \cdots & \ell(p_1,p_N) \\
\ell(p_2,p_1) & \ddots & & \vdots \\
\vdots & & \ell(p_i,p_j) & \\
\ell(p_N,p_1) & \cdots & & \ell(p_N,p_N)
\end{bmatrix}

\mathbf{R}$

where the adjacency function $\ell(p_i,p_j)$ is 0 if page $p_j$ does not link to $p_i$ , and normalized 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, so the matrix is 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 are fast to approximate (only a few iterations are needed).

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 penalize link farms and other schemes designed to artificially inflate PageRank. In December 2007 Google started actively penalizing sites selling paid text links. How Google identifies link farms and other PageRank manipulation tools are among Google's trade secrets.

Computation

To summarize, PageRank can be either computed iteratively or algebraically. The iterative method can be viewed differently as the power iteration method , or power method. The basic mathematical operations performed in the iterative method and the power method are identical.

Iterative

In the former case, at

t=0

, an initial probability distribution is assumed, usually :

PR(p_i; 0) = \frac{1}{N}

At each time step, the computation, as detailed above, yields : $PR(p_i;t+1) = \frac{1-d}{N} + d \sum_{p_j \in M(p_i)} \frac{PR (p_j; t)}{L(p_j)}$ , or in matrix notation : $\mathbf{R}(t+1) = d \mathcal{M}\mathbf{R}(t) + \frac{1-d}{N} \mathbf{1}$ , (*) where $\mathbf{R}_i(t)=PR(p_i; t)$ and $\mathbf{1}$ is the column vector of length $N$ containing only ones.

The matrix $\mathcal{M}$ is defined as : $\mathcal{M}_{ij} = \begin{cases} 1 /L(p_j) , & \mbox{if }j\mbox{ links to }i\ \\ 0, & \mbox{otherwise} \end{cases}$ i.e., : $\mathcal{M} := (K^{-1} A)^t$ , where $A$ denotes the adjacency matrix of the graph and $K$ is the diagonal matrix with the outdegrees in the diagonal.

The computation ends when for some small $\epsilon$ : $|\mathbf{R}(t+1) - \mathbf{R}(t)| < \epsilon$ , i.e., when convergence is assumed.

Algebraic

In the latter case, for

t \to \infty

(i.e., in the steady state), the above equation (*) reads :

\mathbf{R} = d \mathcal{M}\mathbf{R} + \frac{1-d}{N} \mathbf{1}

. (**) The solution is given by :

\mathbf{R} =  (\mathbf{I}-d \mathcal{M})^{-1}  \frac{1-d}{N}  \mathbf{1}

, with the identity matrix

\mathbf{I}

The solution exists and is unique for $0 < d < 1$ . This can be seen by noting that $\mathcal{M}$ is by construction a stochastic matrix and hence has an eigenvalue equal to one because of the Perron-Frobenius theorem.

Power Method

If the matrix

\mathcal{M}

is a transition probability, i.e., column-stochastic with no columns consisting of just zeros and

\mathbf{R}

is a probability distribution (i.e.,

|\mathbf{R}|=1

\mathbf{E}\mathbf{R}=1

where

\mathbf{E}

is matrix of all ones), Eq. (**) is equivalent to :

\mathbf{R} = \left( d \mathcal{M} + \frac{1-d}{N} \mathbf{E} \right)\mathbf{R} =: \widehat \mathcal{M} \mathbf{R}

. (***) Hence PageRank

\mathbf{R}

is the principal eigenvector of

\widehat\mathcal{M}

. A fast and easy way to compute this is using the power method: starting with an arbitrary vector

x(0)

, the operator

\widehat\mathcal{M}

is applied in succession, i.e., :

x(t+1) = \widehat\mathcal{M} x(t)

, until :

|x(t+1) - x(t)| < \epsilon

Note that in Eq. (***) the matrix on the right-hand side in the parenthesis can be interpreted as : $\frac{1-d}{N} \mathbf{I} = (1-d)\mathbf{P} \mathbf{1}^t$ , where $\mathbf{P}$ is an initial probability distribution. In the current case : $\mathbf{P} := \frac{1}{N} \mathbf{1}$ .

Finally, if $\mathcal{M}$ has columns with only zero values, they should be replaced with the initial probability vector $\mathbf{P}$ . In other words : $\mathcal{M}^\prime := \mathcal{M} + \mathcal{D}$ , where the matrix $\mathcal{D}$ is defined as : $\mathcal{D} := \mathbf{D} \mathbf{P}^t$ , with : $\mathbf{D}_i = \begin{cases} 1, & \mbox{if }L(p_i)=0\ \\ 0, & \mbox{otherwise} \end{cases}$ In this case, the above two computations using $\mathcal{M}$ only give the same PageRank if their results are normalized: : $\mathbf{R}_{\textrm{power}} = \frac{\mathbf{R}_{\textrm{iterative}}}{|\mathbf{R}_{\textrm{iterative}}|} =
\frac{\mathbf{R}_{\textrm{algebraic}}}{|\mathbf{R}_{\textrm{algebraic}}|}$ .

Efficiency

Depending on the framework used to perform the computation, the exact implementation of the methods, and the required accuracy of the result, the computation time of the these methods can vary greatly.

Variations

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. The displayed value is not the actual value Google uses so it is only a rough guide. ‘Toolbar’ PageRank is different than Google PageRank because the PageRank displayed in the toolbar is not 100% reflective of the way Google judges the value of a website.

The Google Toolbar's PageRank is updated approximately 4 times a year, so often shows out of date values. It was last updated on 3 April 2010.

SERP Rank

The SERP (Search Engine Results Page) is the actual result returned by a search engine in response to a keyword query. The SERP consists of a list of links to web pages with associated text snippets. The SERP rank of a web page refers to the placement of the corresponding link on the SERP, where higher placement means higher SERP rank. The SERP rank of a web page is not only a function of its PageRank, but depends on a relatively large and continuously adjusted set of factors (over 200), commonly referred to by internet marketers as "Google Love" . SEO (Search Engine Optimization) is aimed at achieving the highest possible SERP rank for a website or a set of web pages.

With the introduction of Google Places into the mainstream organic SERP, PageRank plays little to no role in ranking a business in the Local Business Results . While the theory of citations is still computed in their algorithm, PageRank is not a factor since Google ranks business listings and not web pages.

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 green bar, the Google Directory does not show the PageRank as a numeric value but only as a green bar.

False or spoofed PageRank

While the PageRank 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 was at one time easily manipulated. A previous flaw was that any low PageRank page that was redirected, via a HTTP 302 response or a "Refresh" meta tag, to a high PageRank page caused the lower PageRank page to acquire the PageRank of the destination page. In theory a new, PR 0 page with no incoming links could have been redirected to the Google home page - which is a PR 10 - and then the PR of the new page would be upgraded to a PR10. This spoofing technique, also known as 302 Google Jacking, was a known failing or bug in the system. Any page's PageRank could have been spoofed to a higher or lower number of the webmaster's choice and only Google has access to the real PageRank of the page. Spoofing is generally detected by running a Google search for a URL with questionable PageRank, 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.

The intentional surfer model

The original PageRank algorithm reflects the so-called random surfer model, meaning that the PageRank of a particular page is derived from the theoretical probability of visiting that page when clicking on links at random. However, real users do not randomly surf the web, but follow links according to their interest and intention. A page ranking model that reflects the importance of a particular page as a function of how many actual visits it receives by real users is called the intentional surfer model . The Google toolbar sends information to Google for every page visited, and thereby provides a basis for computing PageRank based on the intentional surfer model. The introduction of the nofollow attribute by Google to combat Spamdexing has the side effect that webmasters commonly use it on outgoing links to increase their own PageRank. This causes a loss of actual links for the Web crawlers to follow, thereby making the original PageRank algorithm based on the random surfer model potentially unreliable. Using information about users' browsing habits provided by the Google toolbar partly compensates for the loss of information caused by the nofollow attribute. The SERP rank of a page, which determines a page's actual placement in the search results, is based on a combination of the random surfer model (PageRank) and the intentional surfer model (browsing habits) in addition to other factors .

Other uses

A version of PageRank has recently been proposed as a replacement for the traditional Institute for Scientific Information (ISI) impact factor, 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 been used to rank spaces or streets to predict how many people (pedestrians or vehicles) come to the individual spaces or streets. . In lexical semantics it has been used to perform Word Sense Disambiguation and 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 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.

The PageRank may also be used as a methodology to measure the apparent impact of a community like the Blogosphere on the overall Web itself. This approach uses therefore the PageRank to measure the distribution of attention in reflection of the Scale-free network paradigm.

In any ecosystem, a modified version of PageRank may be used to determine species that are essential to the continuing health of the environment.

Google's `rel="nofollow"` option

In early 2005, Google implemented a new value, "nofollow" , for the rel attribute of HTML link and anchor elements, so that website developers 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 previously create many message-board posts with links to their website to artificially inflate their PageRank. With the nofollow value, message-board administrators can modify their 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 legitimate comments. (See: Spam in blogs#nofollow)

In an effort to manually control the flow of PageRank among pages within a website, many webmasters practice what is known as PageRank Sculpting - which is the act of strategically placing the nofollow attribute on certain internal links of a website in order to funnel PageRank towards those pages the webmaster deemed most important. This tactic has been used since the inception of the nofollow attribute, but the technique has been thought by many to have lost its effectiveness.

Removal from Google Webmaster Tools

On October 15, 2009, Google employee Susan Moskwa confirmed that the company had removed PageRank from its Webmaster Tools section. Her post said in part, "We’ve been telling people for a long time that they shouldn’t focus on PageRank so much; many site owners seem to think it's the most important metric for them to track, which is simply not true."

Notes

References

Relevant patents

Original PageRank U.S. Patent- Method for node ranking in a linked database - Patent number 6,285,999 - September 4, 2001

PageRank U.S. Patent - Method for scoring documents in a linked database - Patent number 6,799,176 - September 28, 2004

PageRank U.S. Patent - Method for node ranking in a linked database - Patent number 7,058,628 - June 6, 2006

PageRank U.S. Patent - Scoring documents in a linked database - Patent number 7,269,587 - September 11, 2007

External links

Our Search: Google Technology by Google

How Google Finds Your Needle in the Web's Haystack by the American Mathematical Society

Scientist discovers PageRank-type algorithm from the 1940s - February 17, 2010

Category:Google Category:Search engine optimization Category:Reputation management Category:Internet search algorithms Category:American inventions Category:Crowdsourcing Category:Markov models Category:Link analysis

This text is licensed under the Creative Commons CC-BY-SA License. This text was originally published on Wikipedia and was developed by the Wikipedia community.

Email this Page

Page Rank

Duration: 2:28
Published: 2009-09-17
Uploaded: 2010-12-13
Author: m0serious

Another SEO Rapper video....talking about links and how they increase your page rank. lyrics: Chuck back again so tell me what you thank, new song new dance I call it the Page Rank, so you want to get the listing, in the top position, if you pay attention I can show you how to get it, start with your content, I done said it before, 250 words per page and you good to go, add headers and titles, cross links are vital, make it easy to read use list items, keyword rich is how its meant to be, make sure you have good keyword density, double check your navigation make sure your links function, by order of importance I call it link sculpting, unique meta information for all the pages, trying to make sure you cover all the bases, all text links should include keyphrases, make sure you put the links in the right places, inbound links are what you should be chasing, great content and good link baiting, make people link to the site you created, to do that you gotta have great information, I'm a tell you something else that works wonders, Yahoo directory it cost 300 if you a dealer get links from distributors, if you provide a service get content contributors, it always helps to get links from manufacturers, your links should always be adding not subtracting, link quality is better than link quantity, get valuable links thats a good link policy, some people swap links I don't recommend it, others buy links well I wouldn't spend it, keyword stuffing is not a good practice, if you ...

Boost your Page Rank in Minutes

Duration: 7:22
Published: 2007-05-28
Uploaded: 2010-12-12
Author: calgonite33

How to use software directories to generate hundreds of high PR backlinks to your site in minutes.

Google Toolbar - Page Rank Basics

Duration: 5:36
Published: 2009-06-24
Uploaded: 2010-12-06
Author: grntraining101

toolbar.google.com This video tutorial is on the Google Page Rank Tool. The Page Rank tool displays a number 0 through 10 to let you know how Google assesses the importance of the page that you are viewing. This short video includes the following What is Page Rank How to View Page Rank Benefits of Page Rank How to Download the toolbar Features of the toolbar How to Increase Your Page Rank Posted by: Christine McIvor www.christinemcivor.com

pagerank explicado con manzanas

Duration: 1:46
Published: 2009-07-25
Uploaded: 2010-12-08
Author: jayalaf

pagerank explicado con manzanas

Does the PageRank of Twitter profiles matter?

Duration: 2:03
Published: 2010-03-08
Uploaded: 2010-12-06
Author: GoogleWebmasterHelp

Mani in Delhi asks: "I'm seeing Google PR for some Twitter profiles, even a PR of 4 for someone with less than 50 followers and 100 tweets, whereas for some more meaningful profiles there is no PR. Does PR of Twitter profile pages matter?"

What are your views on PageRank sculpting?

Duration: 1:49
Published: 2009-05-28
Uploaded: 2010-10-23
Author: GoogleWebmasterHelp

Rand from Brighton asks: "What are your views on 'PageRank sculpting'? Useful and recommended if implemented right, or unethical?" Recorded on April 23, 2009.

How do PageRank updates work?

Duration: 2:20
Published: 2010-08-26
Uploaded: 2010-12-02
Author: GoogleWebmasterHelp

On what basis you are increasing the PR for each PR update? Made, London

Does Google remove the PageRank coming from links on pages that no longer exist?

Duration: 1:56
Published: 2010-02-04
Uploaded: 2010-12-03
Author: GoogleWebmasterHelp

Robert Enriquez from Charlotte asks: "Websites lose back links due to other websites going out of business or closing (Geocities, AOL, member pages). Does Google remove the back link juice that once came from these pages?"

Is PageRank calculated differently for domains registered before 2004?

Duration: 1:15
Published: 2010-07-12
Uploaded: 2010-12-02
Author: GoogleWebmasterHelp

Is it true that domains registered before 2004 have a totally different way of getting PageRank? ie that "Pre-2004 domains" are highly desirable to buy because they still get PageRank based on old/easier criteria from back then? Land Lubber, Colorado

Seomoz, PageRank Update, Nofollow movement and more

Duration: 3:29
Published: 2007-10-26
Uploaded: 2010-08-26
Author: shoemoney

Pamcakes discusses news for the week of october 26 2007

How does URL structure affect PageRank?

Duration: 2:04
Published: 2010-04-06
Uploaded: 2010-10-23
Author: GoogleWebmasterHelp

Chris. F. Masse from the World-Wide World asks: "On your WordPress.org blog (mattcutts.com), why did you switch from domainname.com/year/month/day/sample-post/ to domainname.com/postname/ ? Has this to do with how PageRank flows within a site?"

Stop The Alexa and PageRank Obsession!

Duration: 7:46
Published: 2009-04-10
Uploaded: 2010-10-27
Author: lisa3876

A fix for PageRank and Alexa junkies. Read Chris G's article on Google rankings. www.chrisg.com

The Google PageRank Mess

Duration: 3:21
Published: 2007-11-02
Uploaded: 2010-08-27
Author: sagerock

www.webmarketingwatch.com Google is doing some crazy things with PageRank these days. Get a quick update of what PageRank is and why these changes are causing a certain amount of insanity in the web marketing world.

Why would an FAQ page rank above a site's homepage?

Duration: 2:10
Published: 2009-11-17
Uploaded: 2010-11-29
Author: GoogleWebmasterHelp

Alex asks: "Sometimes, when you search for a company or so, you won't get the main page as a search result. For example, you search for *example-company-name* and get their FAQ page listed first. How does that happen and how can we avoid it on our own websites?"

Google PageRank Update, Yahoo Unstable, Facebook Ranked One & Memorial Day

Duration: 19:17
Published: 2010-06-06
Uploaded: 2010-12-08
Author: rustybrick

www.SERoundtable.com - This week was a bit slow, due to the Memorial Day holiday but we did have a nice amount of significant changes this week. Google, yesterday, released a Toolbar PageRank update. I reviewed the changes with Google over the past month, including a more detailed explanation of the MayDay update. Google increased the title tag limit to 70 characters. Yahoo Search is becoming really unstable, according to Webmasters. Google let's you advertising your content network ads on the top 1000 sites, the top one is Facebook. Google's Interest Based Ads are really in your face these days. The number one Google organic result is not worth what it was anymore. Google launched App Search in Google Mobile Search. Google was sued over their walking directions. I also displayed the Memorial Day badges, logos and themes from the various search engines. Next week is SMX West, we aren't doing live coverage, but we may do other things. That was this past week at the Search Engine Roundtable. Google June 2010 PageRank Update : www.seroundtable.com June 2010 Google Webmaster Report : www.seroundtable.com Google Increased The Title Tag Limit To 70 Characters? : www.seroundtable.com Yahoo Search Unstable? : www.seroundtable.com Google Lets Advertisers Place AdSense Ads On Top 1000 Web Sites : www.seroundtable.com Did Google Beef Up Their Interest Based Ads? : www.seroundtable.com Why You May Have To Use Google AdWords Even With Top Organic Rankings : www.seroundtable.com iPhone ...

Page Rank vs SERP

Duration: 4:49
Published: 2008-02-28
Uploaded: 2010-11-21
Author: swapw

A lot of people including reputable webmasters have been down playing Page Rank and aim over to SERP. Is SERP really more important? Is Google giving more weights to SERP now? I will tell you my opinion about it and why they're still fundamentally equal. Swapw Swapw - Making money smart swapw.blogspot.com

PageRank Be Gone, Yahoo Paid Inclusion & Bing Webmaster Tool

Duration: 15:39
Published: 2009-10-16
Uploaded: 2010-08-26
Author: rustybrick

www.seroundtable.com - This week was pretty busy over at the Search Engine Roundtable. We broke the news Google has dropped PageRank data from Webmaster Tools and we hope from more places. Yahoo was wrong about the meta keywords tag, they still use it. Yahoo dropped the controversial paid inclusion program. Microsoft Bing has major bugs preventing people from using their Webmaster Tools for over 3 days. Google launched a labs area for Webmaster Tools, adding Fetch as Googlebot and Malware Details. Did Google Suggest just get smarter and start figuring out abbreviations? Google Maps dropped Tele Atlas for their own data in the US Google now only shows 7 of 10 local results in the "ten pack" on web search. Beware of a Google AdSense "account disabled" phishing email that is going around. AVG has labeled some of the Google search results as a security threat. Google thinks Ireland is New Zealand. Google reported earnings, revenue up 7 percent, clicks up 14 percent and cost per click was down 6 percent. A person lost their unemployment check of $405 per week for earning a dollar a day with Google AdSense. A site is conning convicts into paying $50 to remove their name from their web site, which ranks high for their names in Google. That was this past week at the Search Engine Roundtable.

Does Google value its own links for PageRank?

Duration: 1:27
Published: 2009-08-31
Uploaded: 2010-11-22
Author: GoogleWebmasterHelp

Robert Enriquez from Charlotte, NC asks: "Does Google value its own links for PR/Linkjuice? Google Bookmarks, Google Profile, etc. Reason - Google links never appear in Webmaster Tools" Thisvideo is part of a "Grab Bag" series in which Matt Cutts, head of Google's webspam team, answers questions from webmasters. We're not currently taking new video questions, so your best bet for getting an answer about webmaster-related search issues is to head to our help forum: www.google.com

What signals are used in ranking other than PageRank?

Duration: 1:52
Published: 2010-06-02
Uploaded: 2010-12-01
Author: GoogleWebmasterHelp

I see so many people asking "Will this increase my PageRank?". Could you tell them *once again* that ranking is not just about "PageRank" but also 200 different signals? And while being there, why don't you tell us about those 200 different signals? Guillaume, Quebec

Does PageRank take into account cross-browser compatibility?

Duration: 3:21
Published: 2010-08-10
Uploaded: 2010-12-02
Author: GoogleWebmasterHelp

Does PageRank take into account cross-browser compatibility? If a site isn't compatible with certain browsers, does that make a difference for Googlebot? Land Lubber, Colorado

Google PageRank Update Lies

Duration: 4:16
Published: 2007-10-24
Uploaded: 2010-08-26
Author: seobook

from www.seobook.com SEO expert Aaron Wall shares things Google can not lie about.

Apple iAds vs. AdSense, Google PageRank Update & Images In Sitemaps

Duration: 9:16
Published: 2010-04-09
Uploaded: 2010-11-26
Author: rustybrick

www.SERoundtable.com - We are back to our video recaps, despite barely being able to talk. This week I covered that more people are seeing the Google Jazzy blue interface. Also, there was a Google PageRank update and we covered the main changes at Google over the past month. Google Sitemaps is now accepting images. Google graduated malware from Webmaster Tools Labs and made is faster and better. Google AdWords is unbanning some banned AdWords advertisers. Apple is set to compete against Google AdSense for Mobile Apps with Apple iAd. Google Maps dropped the 3D street views, was it a bug or a joke? Yelp is changing their reviews policies in light of all the lawsuits. Finally, Google didn't show an Easter logo this year again, and people were upset. That was this week at the Search Engine Roundtable. More & More Seeing Google's Blue Jazz Interface : www.seroundtable.com Google April 2010 PageRank Update : www.seroundtable.com April 2010 Google Webmaster Report : www.seroundtable.com Google Sitemaps Now Accepting Images : www.seroundtable.com Malware Details Graduates In Google Webmaster Tools : www.seroundtable.com Google Malware Detection Gets Faster & Better : www.seroundtable.com Google AdWords Removing Account Bans? : www.seroundtable.com Apple's iAd to Compete With Google AdSense Mobile Ads : www.seroundtable.com Google Maps Drops 3D Street Views, Was It a Prank? : www.seroundtable.com Yelp To Change Review Policies : www.seroundtable.com No Easter Logo At Google Again ...

Backlinks pagerank serps - google Matt Cutts seo fun video

Duration: 4:49
Published: 2009-04-07
Uploaded: 2010-12-06
Author: tagSeoBlog

Seo-fun video by www.tagSeoBlog.de - Thanks to the google anchorman "the voice" Matt Cutts and his lovely dry "seo webmaster"-videos. Webmaster & SEOs this is a "trash & fun" video. Do not believe in what you see and hear. Production : Martin Mißfeldt ( DUPLICON ) - Germany Music : Claude Debussy "Clair le lune" Please feel free to visit this blog ( Webdesign, SEO & google image search)

Get Backlinks For Top Google Page Rank

Duration: 4:43
Published: 2009-05-20
Uploaded: 2010-09-08
Author: MatthewBredel

seoexciter.com Back Links are so important for elevating the profile of your website. Discover methods of getting the best backlinks and get your website highly indexed by the search engines.

thumbnails

list

Play next
Play all
clear
Autoplay
Autorepeat
Shuffle

Page Rank