**Page Rank Algorithm: How to Calculate the Ranked Page**

PageRank algorithm is a technique for ranking webpages created in the late 1990s by Larry Page and Sergey Brin at Stanford University. PageRank was the foundation upon which Page and Brin built the Google search.

Many years have gone by since then, and Google's ranking algorithms have, of course, get considerably more intricate. Is it still dependent on PageRank? How does PageRank affect ranking, and what might SEOs expect in the future? Now we'll look for and synthesize all of the facts and puzzles surrounding PageRank in order to paint a clear picture. So, we'll do our best.

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**Table of Content:**

1) So why is PageRank Calculated?

2) PageRank formula from the beginning

3) History of Posts

1) So why is PageRank Calculated?

2) PageRank formula from the beginning

3) History of Posts

**4) Is the PageRank algorithm still in use currently?**

**So why is PageRank Calculated?**

This is when things become complicated. The PR of each page is determined by the PR of the pages that point to it. But we won't know what PR these pages are until the PR of the pages leading to them is determined, and so on... When you contemplate that page links can create circles, it appears that this computation is impossible!

But it's not all that horrible.

PageRank, often known as PR(A), is a simple iterative method that correlates to the primary eigenvector of the web's normalized link matrix.

That implies we can go ahead and compute a page's PR without knowing the total cost of the PR of the other pages. That may appear unusual, but each time we execute the computation, we gain a better approximation of the ultimate amount. So all we have to do is memorize each value we compute and repeat the computations until the values stop changing significantly.

Consider the simple network: two web pages, each linking to the other:

Each page contains one incoming link (the incoming count is one, therefore C(A) = 1 and C(B) = 1).

Page A --- Page B

Page B --- Page A

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**CASE: 1**We don't sure what its PR would be to commence with, so let's pick 1.0 and perform some math:

D = 0.85

PR(A) = (1 - d) + d(PR(B)/1)

PR(B) = (1 - d) + d(PR(A)/1)

i.e.

PR(A) = 0.15 + 0.85 * 1 \s= 1

PR(B) = 0.15 + 0.85 * 1 \s= 1

The figures aren't changing at all! So it appears that we began with a fortunate assumption!!!

**CASE: 2 No, that's too simple; perhaps I got it incorrectly (it wouldn't be the first time). Let us just start the estimate at zero and calculates:**

PR(A) = 0.15 + 0.85 * 0 \s= 0.15

PR(B) = 0.15 + 0.85 * 0.15 \s= 0.2775

**NB. Because we've previously computed a "next best guess" at PR(A), we'll utilize it here.**

**And one more:**

PR(A) = 0.15 + 0.85 * 0.2775 \s= 0.385875

PR(B) = 0.15 + 0.85 * 0.385875 \s= 0.47799375

once more

PR(A) = 0.15 + 0.85 * 0.47799375 \s= 0.5562946875

PR(B) = 0.15 + 0.85 * 0.5562946875 \s= 0.622850484375

and so forth. The figures only keep rising. Will the numbers stop climbing until they reach 1.0? What happens if a computation overshoots and exceeds 1.0?

**PageRank formula from the beginning**

Let's have a gander at just how PageRank functions. Every link from one page (A) to the next (B) casts a vote, the power of which is determined by the total weight of all pages that connect to page A. And because we can't know their value until we compute it, the procedure is iterative.

The original PageRank mathematics theory is as follows:

PR(A) = ((1 - d) / N) + d ( PR (B) / L(B) + PR (C) / L(C) + PR (D) / L(D) + ...)

Where A, B, C, and D are some pages, L is the number of links that go to them, and N is the overall amount of pages in the database (i.e. on the Internet).

In terms of d, this is known as the damping factor. Given that PageRank is determined by modeling the behavior of a user who arrives at a website at random and clicks links, we use this damping d factor to represent the likelihood of the user becoming bored and abandoning a page.

As the formula shows, if there are no pages referring to the page, its PR will be greater than zero.

PR(A) = (1 - d) / N

Because it is possible that the user arrived at this site via favorites rather than other sites.

**History of Posts:**

The Internet, particularly online search, is a dynamic environment. Google releases hundreds of updates each year to stay up. Best practices from a decade ago are almost certainly out of date. A year in Online time is equal to 15 years in regular time. YouTube and Facebook did not exist 15 years ago.

PageRank was also becoming obsolete. It was far too simple to manipulate the ranks, and high PR did not always correspond to higher positions. A business might engage a shady SEO firm to create a number of links and impact PR. Some businesses would set up online networks to spread PR domestically and outside.

Google removed PR data from Webmaster Tools in 2009, refer as Search Console.

In 2013, Matt Cutts revealed that the infrastructure for PageRank updates had been broken, that Google no longer considered it vital to update PageRank, and that further updates will be discontinued. PageRank updates are no longer accessible to the general public and have been phased off over the previous few years.

**Is the PageRank algorithm still in use currently?**

It is, indeed. PageRank isn't what it used to be in the 2000s, but Google still places a high value on link authority. Former Google executive Andrey Lipattsev, for instance, suggested this in 2016. A user asked him in a Google Q&A hangout what the primary ranking indicators that Google employed. Andrey's response was obvious.

**Final Words:**

That concludes the PageRank method. I hope you grasped the logic and concept of the PageRank algorithms. Please feel free to post opinions or spread my word.

Thank you!

PageRank, sometimes abbreviated as PR(A), is a straightforward iterative algorithm that corresponds to the major eigen values of the web's normalised link matrix.

That means we can calculate a page's PR despite considering the overall price of the other pages' PR. That may look strange, but each time we do the calculation, we get a better estimate of the final sum. So all we have to do is memorise each number we compute and then repeat the analyses until the values no longer change appreciably.

Consider the following basic network: two web pages, one of which links to the other

#### How is the PageRank algorithm estimated?

PageRank calculates the importance of a webpage by measuring the amount and quality of links to that page. The fundamental idea is that more significant sites are more likely to be linked to by other sites.

#### On what scale is PageRank determined by calculating?

A PageRank score of 0 indicates a low domain, whilst a score of 10 represents only the most reputable sites on the internet. The importance of comprehending PageRank scores is that they are calculated on a logarithmic scale.

#### What much of material is required to rank a page?

According to Forbes, a median of 600-700 characters per page is ideal for SEO. According to Forbes, sites with much less than 300 words per webpage are deemed "thin" by Google and would most likely not rank as well in search. Choosing the appropriate content marketing strategy for SEO may be difficult at best.

#### What does PageRank mean?

PageRank, one of Google's best-known algorithms, plays a significant role in search ranking. PageRank offers numeric values to a site based on a subjective assessment of its links in relation to other variables. This rating is among the elements that determine the page's rank on the SERP.