Understand How Your website Rank with Google Page Rank

PageRank is an algorithm used by Google Search to rank web pages in their search engine results. It’s named after Larry Page, one of the founders of Google. The PageRank algorithm is designed to measure the importance of web pages, and it does this by counting the number and quality of links to a page

The basic idea behind PageRank is that not all links are equally important. For instance, a link from a very popular, high-quality site should have more SEO Juice than a link from a less popular or lower-quality site. PageRank operates under the assumption that more important websites are likely to receive more links from other websites. This allow Google to Rank website between each other and thus provide to user results on his SERP(Search Engine result Page)

How PageRank Works?

At its heart, PageRank is about links both quantity and quality. Each link to your page from another site is like a vote of confidence, a sign to Google that your content is valuable. However, not all votes are created equal. PageRank assigns more weight to links from reputable, high-quality sites than those from less popular or low-quality sites

Initial Assumptions

Initially all pages online have a PageRank score. Historically it is a number given from 0 to 10. But it can start from any value. Each Page on your website has a value for instance, however depends where the pages is located and linked the PageRank will be different.

Iterative Process

Then the PageRank is divided between the number link out of that page. The PageRank is equally distributed to each other page linked from this one and do this process on every page. It is an Iterative Process

The "Dumping Factor"

The PageRank Formula also contains a dumping factor. It describe the fact that a random person surfing online might at any time stop surfing altogether.

This formula of the PageRank is a Mathematic approach to explain it

d is the dumping factor
PR the PageRank in the next algorithm iteration
j is the internet page number if it has one
n the total number of page on internet
i the iteration of the algorithm (Initially set to 0)

The Random Surfer Model

The “Random Surfer Model” is a model used by Google’s PageRank algorithm to decide how much weight to assign to each outbound link on a webpage. This model assumes that not all of a page’s outbound links are equally likely to be clicked by a user, hence the term “reasonable surfer.” In essence, the Random Surfer model predicts the probability that a user will follow a given link on a page, considering factors such as:

Relevance of the Link: Users are more likely to click on links that are relevant to the content they are currently reading. For instance, in an article about gardening, a user might be more likely to click a link leading to a seed store than a link leading to a computer software store.
Position of the Link on the Page: Links placed higher up on the page or embedded within the main content of the page are more likely to be clicked than links in the footer or sidebar.
Anchor Text of the Link: If the text of the link (the anchor text) clearly indicates where the link will lead, users are more likely to click on it. For example, a user is more likely to click on a link that says “Click here for gardening tips” than a link that simply says “Click here.”
Overall Number of Links on the Page: If a page has too many links, each individual link is less likely to be clicked.

The Random Surfer model helps Google’s algorithm assign a more accurate value to each link when determining the PageRank of a page. By doing so, it helps Google deliver more relevant and high-quality search results.

The PageRank ToolBar

At the beginning the PageRank was said to be “Unspam-able” because it was itinitally stated that the score of a page was set by the link value in addition the page content.

With Link Buying Google withdraw that judgment and the PageRank Toolbar was withdrawn by 2016. That being said nobody was able to access Pagerank data anymore but SEO Tool like Majestic was able to recollect the data.