PageRank is an algorithm used by Google. This algorithm is used to evaluate the quality of links. This algorithm was originally developed by Larry Page and Sergey Brin in 1997 to better structure the amount of information available on the Internet at the time and thus improve Google’s ranking algorithm. The PageRank algorithm is based on the original idea that the relevance and authority of a website can be determined based on the number of incoming linking websites.
However, Google has continuously refined this algorithm over time to prevent manipulation and improve the quality of organic search results. Even today, this algorithm is still part of Google’s ranking factors, which is why link building is still considered an important SEO measure.
How the Google PageRank algorithm works
Google’s PageRank algorithm works by first assigning a weight to each linking page. The more pages with the highest possible weighting that link to that website, the higher the PageRank. In addition to the original idea that only the number of links is telegram number database important, Google’s current PageRank algorithm also takes into account the weighting of the individual linking pages. The weighting is composed of various factors, such as the quality or trustworthiness of the content, as well as numerous other factors. These factors ensure that Google can distinguish between particularly high-quality websites that meet the EEAT criteria , for example , and low-quality websites.
PageRank formula and calculation
Various factors are taken into account when calculating the PageRank formula. In addition to the weighting, the damping factor and the number of links play an important role. For example, each page is assigned a value on a scale of 0 to 10. The closer this value is to 10, the better the content and the overall quality and authority of that page. This results in the following formula:
- PR(A) = The PageRank of page A.
- PR(T) = The PageRank of the respective pages that link to page A.
- C(T) = The number of links on the respective page.
- d = The damping factor, which is taken into account via the Random Surfer Model and lies between 0 and 1.
Random Surfer Model
The Random Surfer Model was used until 2010. This is an interpretation of Google’s PageRank algorithm. A random surfer moves through how can core web vitals be improved? the Internet from one page to the next. As the name suggests, the Random Surfer Model selects a random outgoing link on a page with a probability of d . The process is then started on a new random page with a probability of 1-d . This means that the link for each page is always chosen randomly, regardless of its content. In reality, however, the selection is not random, as surfers prefer different content if it is relevant to them. For this reason, the Rational Surfer Model was developed in 2010. This model can depict user behavior more realistically through direct comparison.
Rational Surfer Model
The Rational Surfer Model is a further development australia database directory of the Random Surfer Model and aims to more realistically reflect user behavior by placing greater emphasis on links. Various factors determine which link the user will click on next. Nothing is left to chance. The Rational Surfer Model considers the placement, anchor text, and link visibility. This also makes PageRank manipulation significantly more difficult.
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