Google Pagerank Patent and Search Rankings Evolution

Google's PageRank patent, filed in 1998, introduced a groundbreaking algorithm that revolutionized the way search engines ranked websites. This innovation changed the game for online visibility and credibility.

The PageRank algorithm assigned a numerical score to each website based on its importance, with higher scores indicating greater authority and relevance. This scoring system was calculated by analyzing the number and quality of links pointing to a website.

As a result, websites with many high-quality links pointing to them received higher PageRank scores, making them more likely to appear at the top of search engine results pages (SERPs). This led to a significant shift in the way websites optimized their content and link strategies.

The PageRank patent also introduced the concept of "link equity", which referred to the value or authority passed from one website to another through a link. This concept remains a fundamental aspect of search engine optimization (SEO) to this day.

The history of Google PageRank is a fascinating story that spans over two decades. It all began in 1976 when Gabriel Pinski and Francis Narin first suggested formulating a link analysis problem as an eigenvalue problem.

PageRank was developed at Stanford University by Larry Page and Sergey Brin in 1996 as part of a research project about a new kind of search engine. The algorithm was influenced by citation analysis, early developed by Eugene Garfield in the 1950s at the University of Pennsylvania.

Sergey Brin had the idea that information on the web could be ordered in a hierarchy by "link popularity": a page is ranked higher as there are more links to it. This idea was not new, as a small search engine called "RankDex" from IDD Information Services designed by Robin Li was already exploring a similar strategy for site-scoring and page ranking since 1996.

PageRank was patented in 1998, and the name "PageRank" plays off of the name of developer Larry Page, as well as the concept of a web page. The patent is assigned to Stanford University and not to Google, with Google having 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. The patent was assigned to Stanford University, and not to Google, with Google having exclusive license rights on the patent from Stanford University.

Robin Li's work on RankDex would be referenced by some of Larry Page's U.S. patents for his Google search methods. PageRank was revolutionary at the time, but it obviously wasn’t the only thing that made Google so successful.

The algorithm was first published in a paper in 1998, describing PageRank and the initial prototype of the Google search engine. The paper was co-authored by Rajeev Motwani and Terry Winograd, and it was a major milestone in the development of Google.

PageRank was a significant departure from existing search engines that ranked results based primarily on how many times a search term appeared on a webpage. It treated the internet like a graph, with pages as nodes and hyperlinks as connections between them.

The algorithm was influenced by Hyper Search, developed by Massimo Marchiori at the University of Padua. Jon Kleinberg published his important work on HITS in the same year PageRank was introduced, 1998.

PageRank was revolutionary at the time, but it obviously wasn’t the only thing that made Google so successful. The algorithm has undergone significant changes over the years, but its fundamentals still lie at the heart of SEO.

PageRank works by analyzing the links between pages, with each link casting a vote that depends on the collective weight of all the pages that link to the page it's pointing to.

The original PageRank formula is a complex equation that takes into account the number of links going out from each page and the total number of pages in the collection. This formula is: PR(A) = (1 - d) / N + d * (PR(B) + PR(C) + PR(D)) / (L(A) + L(B) + L(C) + L(D)), where A, B, C, and D are some pages, L is the number of links going out from each of them, and N is the total number of pages.

The PageRank score is also influenced by a damping factor, d, which represents the probability of a user getting bored and leaving a page. This factor is applied to simulate the behavior of a user who randomly gets to a page and clicks links.

The original PageRank formula is quite complex, but it can be simplified to understand its core concept. Each link from one page to another casts a vote, with the weight of the vote depending on the collective weight of all pages that link to the first page.

The mathematical formula of the original PageRank is: PR(A) = (1-d) / N + d \* (PR(B) / C + PR(C) / L + PR(D) / L), where A, B, C, and D are some pages, L is the number of links going out from each of them, and N is the total number of pages in the collection.

The formula shows that the PageRank of a page is influenced by the PageRank of other pages, which is why the process goes in cycles. We can't know the weight of all pages that link to a page till we calculate it, so the process goes in cycles.

The damping factor, d, is a crucial element of the formula. It's the probability of a user getting bored and leaving a page, which is applied to the PageRank calculation.

Internal linking is a crucial aspect of how PageRank works. It's a way to link to your website's content on your own website.

Internal links do influence PageRank, but they don't increase your website's overall PageRank score. However, a good internal linking structure will ensure that your PageRank score is properly distributed throughout your website.

A well-structured internal linking system can help you make the most of your PageRank. It's like a map that helps search engines understand your website's content and how it's all connected.

Google's approach to search rankings has undergone significant changes over the years. They initially used the PageRank algorithm, which analyzed votes from other pages across the web to determine a website's importance.

These votes were not equal, with a vote from The New York Times holding more weight than one from a local blog. This approach was meant to mimic a form of democracy on the web, where high-quality content is rewarded.

However, as Google gained market share, bad actors began to exploit the system by creating artificial signals to boost their website's rankings. This led Google to balance PageRank with other ranking factors and manual reviews.

In 2006, Google made some serious changes to PageRank that had a big impact on search rankings. Google filed an updated patent for an upgraded version of their PageRank algorithm, which marked the beginning of a new era in search engine optimization.

The original PageRank algorithm was retired before the end of 2006, making way for the new and improved version. This change made it harder for spammers to manipulate search rankings through link trading and link farming.

Certain sites, like the New York Times and Google Directory, were considered "trusted" and their links carried more weight than others. When these trusted sites linked to a page, that page gained some trust as well.

The new PageRank algorithm allowed trusted sites to pass on their credibility to other sites through links. This added a new layer of complexity to search engine optimization, making it harder for black hat tactics to succeed.

The original PageRank patent expired in 2019 without any attempt at renewal, marking a significant shift in Google's approach to their algorithms.

This event occurred after a period of secrecy from 2006 to 2019, during which Google became tightlipped about their algorithms.

The expiration of the patent suggests that the original algorithm has been heavily reworked, despite Google's liaisons reporting that the original algorithm is still in use.

Google's approach to search rankings has undergone significant changes over the years, particularly with the rise of "Black Hat" SEOs who exploited PageRank for their own gain.

The original PageRank algorithm was designed to assess a website's importance based on votes from other sites, but it was later balanced with other ranking factors and manual reviews to prevent abuse.

Google's current ranking methodology is no longer transparent, unlike the PageRank algorithm which was filed in a patent and shared publicly.

The company now uses over 200 signals and various techniques to determine a website's ranking, making it impossible to tell exactly how they'll rank a particular web page.

The original PageRank patent expired in 2019 without a renewal, suggesting that the algorithm has been heavily reworked over the years.

Google's philosophy page still mentions PageRank as a key part of their approach, describing it as a form of democracy on the web where each new site is another point of information and another vote to be counted.

However, Google doesn't value votes equally, with a vote from a reputable source like The New York Times carrying more weight than a vote from a smaller site.

Clickthrough rate isn't a confirmed ranking signal, but many SEOs think it may be.

The Reasonable Surfer Model used in Google's PageRank algorithm has used user behavior data, including navigational actions associated with links. This model has been extended multiple times, suggesting Google still sees value in it.

A user's behavior on a page that links to you matters in some way, but it's unclear if this refers to a link's clickthrough rate. Many SEO experts choose to interpret it in that way, possibly due to the patent's ongoing extensions.

Google's algorithms are designed to ignore spammy links, but ignoring too many links can still lead to a manual action. This is a risk you take if your website's backlinks get ignored too much and too often.

Google's anti-spam algorithms can now automatically ignore random links collected over the years, including negative SEO attempts by competitors. However, this doesn't mean you have nothing to worry about.

To prevent vulnerability and spam, you should regularly check your backlink quality and disavow links that are reported as harmful. You can use a backlink checker like SEO SpyGlass to identify high- and medium-risk links.

Google's algorithms are getting smarter, and they can now ignore certain spammy links when calculating PageRank rather than downranking the whole website.

As John Mueller said, "Random links collected over the years aren’t necessarily harmful, we’ve seen them for a long time too, and can ignore all of those weird pieces of web-graffiti from long ago."

However, having too many spammy links ignored can still lead to a manual action, especially if they're on a large scale.

Manual actions are reserved for cases where an otherwise decent site has unnatural links pointing to it on a scale that is so large that Google’s algorithms are not comfortable ignoring them.

To figure out what links are triggering the problem, you can use a backlink checker like SEO SpyGlass and check the Penalty Risk section.

High- and medium-risk backlinks can indicate potential issues, and clicking the "i" sign will show why the tool considered the link bad.

Disavowing links can help, but it's not always necessary to disavow every single spammy link.

If your website’s backlinks get ignored too much and too often, you still have a high chance of getting a manual action.

Google's algorithms can account for situations where a website has naturally picked up some low quality links over time.

However, buying a large quantity of backlinks from a dubious source can damage your PageRank or have no impact at all.

A massive data leak from Google has left many wondering about the inner workings of the search giant's algorithm. The leak revealed a specialized version of PageRank called PageRank_NS, which helps Google evaluate the relevance of pages within specific content clusters.

This variant is particularly effective for categorizing low-quality pages or niche topics, allowing Google to assess their relevance and impact on a site's overall quality with greater precision. By clustering and identifying underperforming pages, PageRank_NS can help Google distinguish them from high-quality content.

The legacy of the old Toolbar PageRank still subtly influences page rankings, despite Google's public discontinuation in 2016. Variations like "rawPagerank" and "pagerank2" continue to play roles in Google's internal ranking algorithms.

Google categorizes links into different quality tiers, with click data determining the tier and influencing the PageRank flow and impact on search rankings. This means that user interactions can significantly affect search results, making click data a crucial factor in determining a site's ranking.

Google is well-aware that many SEOs want to game their algorithm, so they try not to share their secret sauce. However, experienced SEO professionals have some theories about what works.

Many of the factors influencing PageRank are speculative, and Google doesn't publicly confirm them.

Google's algorithm considers the number of links pointing to a page when calculating PageRank.

The quality of links also matters, with high-authority sites carrying more weight than low-authority sites.

PageRank is not just about the number of links, but also about the relevance of the content linked to.

Google's algorithm takes into account the anchor text used in links, which can indicate the topic of the linked page.

Experienced SEO professionals believe that the age of a domain can also influence PageRank, with older domains potentially having more authority.

PageRank is still applied today, although it's not the same as it was in the early 2000s.

Google relies heavily on link authority, and a former Google employee, Andrey Lipattsev, confirmed this in 2016, stating that content and links pointing to a site are the main ranking signals.

In 2020, John Mueller confirmed that Google uses PageRank internally, among many other signals.

Google employees keep reminding us that there are many other ranking factors, but SEOs continue to consider PageRank a strong ranking signal.

It's interesting to note that Google employees might be downplaying the importance of PageRank to shift attention away from link spam, but SEOs are too savvy to fall for that.

As a result, SEOs continue to focus on building high-quality backlinks, using tactics like PBNs and grey-hat tiered link building.

PageRank may not be the only ranking signal, and it may not be the most important one, depending on the query, but it's still a crucial part of Google's overall search algorithm.

John Mueller's 2020 tweet confirmed that PageRank is used internally, but many other signals are stronger.

As we dive into the fascinating world of Google PageRank, one interesting observation is that the algorithm was initially designed to measure a web page's importance based on the number of links pointing to it.

The PageRank patent describes the algorithm as a "random surfer" model, where an imaginary surfer randomly clicks on links to navigate the web. This model was used to assign a score to each web page, with higher scores indicating greater importance.

Google's founders, Larry Page and Sergey Brin, were Ph.D. students at Stanford University when they developed the PageRank algorithm. They were trying to solve the problem of how to rank web pages in a meaningful way.

The PageRank algorithm uses a damping factor of 0.85 to prevent infinite loops of links. This means that 85% of the time, the surfer will follow a link, and 15% of the time, the surfer will jump to a random page.

The algorithm also takes into account the number of links on a page, with more links indicating a lower score. This is because a page with many links is likely to be a hub or a directory, rather than a high-quality source of information.

Google's PageRank algorithm is a complicated formula, but we do know it's based on the concept of backlinks and forward links. For example, if documents B and C link to document A, A is a forward link of B and C, and B and C are backlinks of A.

The algorithm assigns a PageRank score between 0 and about 65,000 to a site. While Google doesn't share a site's precise score, they did provide a simplified score for Google Toolbar users in the early 2000s.

A PageRank of 1 meant a site had few links, and the links it had were low value. A PageRank of 10 meant a site had an incredible number of high-quality links pointing to it.

The original PageRank patent shows a typical relationship between three hypertext documents A, B, and C. This patent is still the basis for Google's improved version of the algorithm.

Google used to openly present a site's PageRank score to anyone who downloaded their free toolbar. This helped users understand how Google valued their website.