The website’s page ranking was popularized by Google to make a website be noticed through the Google search engine’s recommendation. A new website needs connections to be found by the searcher. Newly established sites require publicity or a recommendation from a known personality, just like a newly opened business in town, to be able to leave a mark on what kind of business it is and what it does. Page Rank by Google, or PR, is the most complex patent that the company has made. It is the most common practice among SEOs to establish good page traffic and links from other popularly ranked pages. Page ranking depends on feedback and search, internal links, social media posts, content creation, and ads to affect the page score.
The search engine uses it to recommend as per ranking who has the best to offer for the searcher. Google invented and popularized how search results are and how web pages were relevant to the search that was made. PageRank uses an algorithm to identify or measure the popularity of a website and its relevance. PageRank works by identifying the right keyword linked to a specific and relevant website that the searcher needs. Sergey Brin and Larry Page invented Google’s PageRank in 1998; they developed the iteration made based on Kleinberg’s early works.
What is the definition of PageRank?
PageRank or PR is an algorithm design used by the Google search engine to rank a web page that has quality keywords and considers links as relations from other websites that point to the specific web pages. It impacts newly published websites that are going to be ranked by using ads and promotions from established websites. PageRank sees the importance or authority of the page based on how it is linked with other known web pages; suggested websites are identified by their score. The HITS, or Hyperlink Induced Topic Search in the early works of Kleinberg, inspired the development of PageRank by Google. PageRank algorithm measures how important the website is, and it sees how it is linked between pages or if it is linked by a known website that makes it searchable. PageRank is needed because the algorithm enables websites to rank fairly. It is because of its importance to other websites. The Alta Vista search engine was the most used search engine in 1995; it faded out after Google arrived, then later it was sold to Yahoo, keeping its name. The Alta Vista was not designed for the business. The search made from Alta Vista does not have a complex algorithm compared to the capability of Google.
How is the History of PageRank Algorithm?
Sergey Brin and Larry Page, the founders of Google, developed PageRank, in 1996. The PageRank wasn’t a new algorithm based on history dug by Massimo Franceschet, long before the PageRank was used by Google; there are known names that developed the familiar method in their own field. Wassily Leontief is an economist who developed a method to rank a country’s industrial sectors by how important they are in other industries on how they manufacture their products. Leontief’s method was later awarded the Nobel Prize for economic works. A method that identifies a person’s importance through endorsements from important or known people, sociology, and bibliometrics was published in 1965 by Charles Hubbell. Later, the method of Hubbell expounded on the bibliometrics foundation of ranking, through people’s journals and its importance, the same reasoning that a PageRank was developed by Gabriel Pinski and Francis Narin. It improves over time as new technology is used. Jon Kleinberg’s theory came up with a similar approach to Brin and Page, with their publication of the so-called PageRank. Jon Kleinberg of Cornell University published a method called, HITS or Hypertext Induced Topic Search. HITS has received recognition, though both PageRank and HITS have been developed separately in a similar approach inspired by the early works and development of recognizable people. Google’s Brin and Page of Stanford University’s development of PageRank receive recognition, along with Kleinberg’s HITS. Sergey and Larry acknowledged the similarity of their methods in their own paper. Later, PageRank became the widely renowned search engine of Google.
Who invented PageRank Algorithm?
Google’s founders, Sergey Brin and Larry Page invented and popularized the importance of PageRank for the website’s ranking through web searches. Their inspiration for the early concepts was implemented on PageRank and was improved as the modern age algorithm for searches, businesses sought its importance in cyberspace. The purpose of PageRank is to effectively retrieve score data that the webpage has, and how it is linked to other websites.
Who named PageRank Algorithm?
Larry Page, one of the founders of Google, named PageRank as the primary algorithm to rank web pages according to their score. Larry, see the importance of PageRank on how to implement searches with the user search browser. PageRank, named after Larry Page’s last name, authored the key element that the Google search engine is going to be known for. Sergey and Larry see the potential methods that the early authors did; those people were based on their field of expertise. Larry Page incorporates the ideas and implements them in the new technology, and it succeeded. Today, Google still uses the blueprint of PageRank as a search algorithm, but is more complex and not the same anymore as PageRank does, decades ago.
RankDex is used by most search engines since 1996; it is the page-ranking method introduced by Robin Li. RankDex was the first web search provider from 1996 until 2001. RankDex was the early search engine that applied hyperlinks, as the primary element, to determine the quality of a website it was referring to. Rankdex influenced PageRank’s development and was cited on Google’s patent, then later overcome its capability. Li decided to use RankDex technology for a search engine called Baidu, as the years passed. Baidu is the primary search engine used in China. RankDex remains the early application of Google’s PageRank by using hyperlinks as the basis of quality endorsements received by webpages. RankDex was awarded its patent in 1996; then two years later, Google’s PageRank was patented with a similar approach as RankDex. Larry Page referenced RankDex and acknowledged Li’s work for influencing the outcome of PageRank.
What is the PageRank Algorithm Formula?
Below is the formula of the PageRank algorithm.
PR(A) = 1-df/Np +df (PR(B)/Ln(B) + PR(C)/Ln(C) + PR(D)/Ln(D) + …)
PR stands for PageRank, while the A, B, C, D, etc; are examples of relative pages; while, df is the term used to describe damping factors. The Ln stands for the number of links on each page, while Np is the number of pages used within the formula. It gives a formula if a newly published article is ranked,
PR(A) = 1-df/Np +df
An Example of Simplified PageRank Algorithm
Below is the simplified formula example of the PageRank algorithm.
PR(A) = PR(D)/L(d) + PR(B)/L(b) + PR(C)/L(c)
Where PR(A) is the PageRank of A, which is to be determined, while PR(D), PR(B), and PR(C) is the rank score of existing pages linked to the PR(A). While L (d), L (b), and L (c) are the number of links that pages D, B, and C have.
What is Damping Factor in PageRank Algorithm Formula?
The Damping Factor, or df or d, is the PageRank identifier of a web surfer’s manner. Upon clicking, from website to website, and then eventually stopping, the probability to continue is defined as the damping factor. It completes the algorithm’s factor of determining the PR or PageRank of a specific website. The damping factor sees the probability of a web surfer jumping on a node that has no links.
How to Compute PageRank?
PageRank is computed based on the other website backlinks, either websites or pages. It is computed using a formula along with the number of links that a page has and damping factor probability, and the existing rank of the linked pages. The formulation is based on PR(A) = PR (D)/Ld + PR (B)/Lb + PR (C)/Lc is going to be implemented below.
Listed below are the steps on how to compute PageRank.
- Identify the given data with the formula, where PR(A) is unknown, PR(D)=8, PR(B)=5, PR(C)=4. The links between pages are L(d)=3, L(b)=5, and L(c)=2.
- Implement the given values into the formula, PR(A) = PR(D)/d + PR(B)/d + PR(C)/c.
- The implemented values be, PR(A) = PR(8)/3 + PR(5)/5 + PR(4)/2.
- Follow MDAS rule, multiplication and division first, then addition and subtraction next.
- As follows, PR(A) = 2.67 + 1 + 2
- The final answer is PR(A) =5.67. The PageRank of website A is 5.67.
How to Compute PageRank Iteratively?
An iterative computation method is a procedure in mathematics that includes an initial value to generate an approximation to improve the solution; it is termed the power method. The iterative computations are the same as algebraic ones, including their operators.
Below is how the Iterative computation for PageRank.
The iterative formula uses the same identifier, such as PR for PageRank, and p is the power value, that begins the computation for the 0. The iterative method of computing is to repeat its computation until its occurrence ends. The initial value is 0 then repeats the computation up to its convergence.
How to Compute PageRank Algebraically?
For determining PageRank calculations for website pages, an algebraic method is suggested. The quantity of calculation in the proposed method is independent of the damping coefficient value, allowing for more precise estimates of PageRank rankings in contrast to the analogs. The proposed method stands out for performing calculations step-by-step, while the graph traversal algorithm is doing its job to illustrate how the trend goes.
As illustrated above, N is the number of pages, while d states the damping factor that is included in the algorithm. Pi and Pj are the PageRank of compared web pages, while the L is the links from each page to the P.
How to Compute PageRank with Python?
Listed below is how PageRank is computed with Python.
- Import BeautifulSoup as it is used for web scraping and NetworkX from the library is used for creating a graph structure for the web page with Nodes as Web Pages and Edges as Links to the pages. To calculate the number of edges and nodes and PageRank.
- Start by Initializing the Graph using the Graph() method in the Library. Add the URL as a node in the Graph for which the PageRank needs calculating.
- The values pagerank_numpy(), number_of_edges(), and number_of_nodes() methods from the Library are used to get PageRank, a total number of edges, and nodes respectively.
- The Nodes in the graph are given different values based on their importance.
- It is time to draw the graph with nodes and edges using the draw() method in the Library with labels parameter set to True if the URLs require graph representation.
Import networkx as nx
From bs4 import BeautifulSoup
g=nx.Graph()
g.ad_node(root_url)
Pagerank = nx.pagerank_numpy(g, alpha=0.85, personalization=None, weight=’weight’, dangling=None)
edgeNumber = g.number_of_edges()
nodeNumber = g.number_of_nodes()
nodesize=[g.degree(n)*10 for n in g]
nx.draw(g,with_labels=False)
nx.draw_networkx_nodes(g,pos,node_size=nodesize,node_color=’r’)
nx.draw_networkx_edges(g,pos)
How to Compute PageRank with MATLAB/Octave?
The PageRank score provides a notion of the relative value of each node based on the illustration of how a node is related to the other nodes. The theoretically-defined PageRank score is the minimal chance that eventually there are some user that randomly clicks links on every website.
Below is an example of how to compute PageRank with Matlab.
function [v] = rank2(M, d, v_quadratic_error)
N = size(M, 2);
v = rand(N, 1);
v = v ./ norm(v, 1);
last_v = ones(N, 1) * inf;
M_hat = (d .* M) + (((1 – d) / N) .* ones(N, N));
while (norm(v – last_v, 2) > v_quadratic_error)
last_v = v;
v = M_hat * v;
end
end
The M matrix where M_i,j exemplifies the link from “j” to “i”, such that for all “j” where sum (i, M_i,j) = 1. Parameter d is the damping factor, while parameter v_quadratic_error quadratic error for v. The vector of ranks is v, such that v_i is the i-th rank from [0, 1]. N is equal to either the dimension of M or the number of documents.
What are the Variations of PageRank?
PageRank has different sorts of variations on calculating the ranks of pages; these determine the PageRank in response to how the values changes, depending on the quality of the links and the importance of the recommender.
1. Google Toolbar PageRank
Google announced the Google toolbar’s PageRank later in 2016 to be removed from its browser. According to Google, they are just placing it internally; for their purposes, it wasn’t any more available to any individual as a basis of a webpage’s ranking to Google. The purpose of a visible PageRank is to show how important the current website is. PageRank has a bar that rates from 0 up to 10, and 10 is the highest score. A 10 score indicates a full green bar on the PageRank toolbar, while a zero remains just empty or white. Webmasters benefit from the features that Google provides, but due to its saturation as SEO’s basis for businesses, Google removed the indicator, but Google provided Google console, which was more analytical and broad.
2. Undirected Graph PageRank
Undirected Graphs for PageRank are used as a basis for the algorithm formulas that are presented with graphs without loops. These undirected graphs support the mathematical computations to represent the PageRank score of a node. These undirected graphs are one of the ways to analyze the importance of a page or website. Typically, direct graphs are used for PageRank, but in some cases, undirected graphs seem to be proportional to the vertices, or degrees. Undirected graphs are not often used as a primary indicator for PageRank values but as a support for the directed graph.
3. Distributed Algorithm PageRank
The distributed algorithm for PageRank is done using each computer’s processor and memory to compute a problem and to determine the PageRank through other computers. Distributed algorithms are not able to apply to some iterative solutions due to their difficulty in traditional matrix-vector multiplication styles for iterative methods and other bandwidth restrictions and convergence rates.
4. Generalized PageRank with Eigenvector Centrality
Eigenvector centrality is a variant of PageRank; it does have similarities in the node’s connection but has a different effect on the results of the ranking. An example on a social network, if one person has 50, and the other has only 1, but his only friend is a known personality. The person’s social network eventually grows because of the connection to a popular person. Compared to Eigenvector centrality, a node accumulates, or receives a relation to the other connecting node, but gives importance to the outgoing node under its relation.
5. Google Directory PageRank
The Google Directory is a discontinued web directory; last July 20, 2011, it was used by most SEO and webmasters to determine the PageRank. It is considered the most preferable data to look at because of most issues regarding the PageRank toolbar’s accuracy. The Google directory provides all details regarding the description of the website, including the PageRank.
6. Spoofed PageRank
PageRank spoofing is a type of manipulation or flaw in the system to generate such a PageRank level. Spoofing is present during the earlier time when the 302 error server links are used to be considered as backlinks. It eventually generates a PR10 for most websites, even newly published sites. PageRank’s manipulation is a type of unnatural way of link endorsement or recommendation; rather, cheating. There are websites that offer links, in exchange for cash; the PageRank of a newly developed webpage ranks up but does not have the quality links that an established website has.
7. Manipulated PageRank
PageRank’s manipulation is because of sites that want to rank up immediately. There are so-called services that other websites offer that intentionally trick PageRank. Large websites sell links to other small websites to start ranking up in just a short amount of time. The trade of link farms had become more saturated and the search results became more irrelevant. It is why Google had to ditch the toolbar and repurpose it to provide a more complex tool known as the Google console.
8. Directed Surfer Model PageRank
The directed surfer model is a more intelligent user who stochastically navigates between pages based on the content and the search phrase used. The approach of a directed surfer model is based on the PageRank score of a page that is dependent on the query. The directed surfer model is provided with multiple queries and selects following the probability to guide its behavior for taking some steps. It chooses another term in accordance with the factors to determine its behavior.
9. Random Surfer PageRank Model
The objective of the random surfer is to go from one web page to another. It is similar to the Markov chain model because of the prediction probability that a user visit based on the directed graph and matrix. The model describes a random visitor’s behavior visiting a web page; the goal is to calculate the probability of how it goes from one page to another. The random surfer model for PageRank provides a basis for the algorithm, to determine the proper score of such web pages, to respond to contributing links, a sign of authority for the page.
10. Personalized PageRank
Personalized PageRank is an individual or preference type of model that already had a preset value to know how it behaves as a visitor of a webpage. PPR is a method used in graphical mining and network analysis for node proximity instances. In an example; the nodes are A, and the target node is B, the instance probability is π (A, B) as a value of PPR. In the representation of a random surfer visiting A, it ends at B, giving bidirectional importance between the two nodes.
What are the Social Components of PageRank?
PageRank systematically is run by nodes and those nodes are affected by page visits, redirecting links, and purpose links as well. The system that is structuralized by the PageRank algorithm shows that human interaction is the purpose of setting such nodes. Social relationships and economic status make the chain of suggesting information revolve freely. PageRank’s importance succeeded because of the social components it represents. PageRank is a kind of social network that connects every individual with similar attributes gathered in one place, according to Katja Mayer. Mayer sees a social relationship build-up between people and PageRank, as it creates an individual preference every time and person visits a website of choice, and then eventually PageRank learns that preference as a guide for suggesting good websites.
Attention Economy is, what Matteo Pasquinelli suggests, about the social components that lie within PageRank. The product is going to be in demand and people buy that because of the focus Attention Economy has if a particular product receives high attention. Any suggestions influence the decision-making, as viewers see relationships with PageRank. The economic growth is parallel to PageRank’s algorithm on how people think and on how people see suggestions and recommendations.
How does Search Engine use PageRank for Crawling?
Web Crawler is a term used to describe robots that are programmed to crawl through websites and new websites. PageRank determines ranked information by crawlers and saves it in their library, called data banks. The crawling method used by search engines helps to provide more quality searches by identifying ranking sites with quality content. The web crawling method enables indexing of PageRank so that when the search is queued, the results are immediate and efficient according to rank. The crawling method used by search engines indexes categorical information such as location, language, and previously searched data. Web crawlers are just robots that collect web page information and index a relative website, according to Martin Splitt of Google Search Relation. The latest quote made by Martin Splitt in a YouTube interview with Barry Schwartz of Search Engine Land was, “Don’t reinvent the SEO wheel”. Split reiterates that some SEO developers are trying to solve something that wasn’t a problem in the first place. Search engine crawlers do not see or determine the quality of the page other than the PageRank itself. The web browser plays a big role in providing quality information for the web server, by contributing to the referral perspective of the web user assigned to a specific computer.
What are the Academic Researches and Papers for PageRank
These are the academic research and papers for PageRank.
- The PageRank Citation Ranking, Bringing Order to the Web, by Stanford published January 29, 1998: The research describes how PageRank sees the quality of web pages, through its objective and mechanically how it behaves. The research determines how the interest and attention of a person affect the algorithm compared to a random web surfer. The research shows efficient PageRank computation for a large number of pages and through navigation illustrates how the PageRank is applied.
- PageRank as a method to rank biomedical literature by importance, by National Library of Medicine, published December 9, 2015: The research shows that managing and handling article overload see the importance of optimal ranking of literature. Based on their current ranking method, originally are raw citations summing inbound links but not giving importance to the citations or internal references. PageRank algorithm is able to be applied to bibliometrics, to sum up the important references within the network.
- An Efficient Ranking Algorithm for Scientific Research Papers, by Zarqa University-Jordan published March 2016: The main objective of their thesis is to propose an efficient ranking method suitable to rank Student Research Papers (SRP) and able to improve PageRank by including the author’s score and by making it less biased comparing to new papers.
- An improved PageRank algorithm for Social Network User’s Influence research: by Changchun University of Science and Technology 2015: According to their research, the PageRank algorithm improves the social media user’s influence on microblogs, user groups, and structure nodes on the internet. The experiment, it shows that improvement in algorithms makes good occurrence and significance with the microblog users.
- PageRank variants in the evaluation of citation networks, by University of West Bohemia, Department of Computer Science and Engineering 2014: The research goal was to explore a possible approach to calculating the rank of research papers by renowned authors. The referral outcome upon the evaluation is the same or somewhat closer according to human interpretation. The PageRank algorithm and its variations were used for various citation networks during an evaluation. The goal was to determine if the results were based on the author network or the publication network.
- Focused Page Rank in Scientific Papers Ranking by University of Trento, Italy published in 2008: The research objective is to use the FPR or focused Page Rank algorithm to rank scientific papers according to the focus surfer model. The Focused Surfer Model believes that the issue is with outgoing links. The FPR algorithm points out that highly cited scientific papers draw more views and attract further citations.
What are the PageRank Affecting Factors?
These are the factors affecting PageRank.
- Good Website: A good website has essential sections that the code provides. A good structure of the website is based on the builder and type of format that the website has. Web crawlers do their job to retrieve helpful data like structure, content, internal links, and PageRank. The website is well-coded, providing all the sections that a website’s structure. A good website has a robots.txt file, which is a file with the website information, as the crawlers do not keep sending requests for information. Crawlers update the robots.txt file and don’t need to be cleared for any reason unless rebuilding the website information.
- Quality Links: Backlinks from a good website are one of those key elements that influence the rankings or the PageRank of a Web page. The PageRank algorithm analyzes the link as a quality link that influences the quality of the recommending website if those links are directed to a high-quality site. Linking to the same or similar web pages lowers the PageRank of a website, as it is expressed as spam. A blog post or article with content-related links helps the build-up of the website’s reputation.
- Website Loading Speed: Before launching a Website on the internet, make sure that the Website is fast enough to load in real-time, especially on mobile devices. Primarily is one of the factors that are not to be taken for granted as it impacts the overall ratings of a Page. Developers that design pages are going to implement the Mobile-First index. The design, structure, and implementation have to be enabled as a mobile-first design before establishing a desktop version. The transition from mobile to desktop orientation be easier.
- Page Age: Most of the websites that rank higher are those established already in years, because of the gain that is received over time for maintaining good quality content. Google penalizes web pages that are not providing related content and quality links. The Page’s age determines the PageRank because of the accumulation of good traffic, recommended links, and content.
- Unique and Meaningful Content: Google emphasizes that good quality content makes the website rank. Providing unique and meaningful content is not only helping with SEO ranking but establishes regular visitors to the Website as well. These visitors become followers because of the meaningful content that enables them to come back. Spammy-type pages do not last longer or maintain good PageRank, as it eventually is detected by the algorithm because of redirected links from unknown landing pages or websites.
- Don’t use Cloaking or IP Delivery: Cloaking and IP delivery methods are an old-fashioned way of deceiving web crawlers. Using IP cloaks objectively is to divert or trick the information gathered by the search engine through those web crawlers. Google’s AI technology detects these kinds of tactics that SEO builders use, and as a consequence, it ruins the reputation or rank of the website for not giving rightful information instead. Google’s reputation is to provide the right content for all searchers, instead of being a deceiver by using such methods. Applying the tools Google provides makes life easier, and thus lying is going to have a bad outcome, in the end, making all the work have to be reset from the beginning.
- Keywords Title Tags: Title tags are given as an SEO tool to be optimized and used. Providing a meaningful and related keyword is essential to affect PageRank. Short phrases that describe the content do much for the article or blog to be recommended in the search page results.
- Keywords: Though Google has stated that crawlers already determine the content of a certain website, keyword-focused content drastically makes changes with attention. Using the keywords for about 3% of the content helps the content get noticed on Google research. Keywords are based on the topic or title of the content; consistency has to be present, and the quality is always there too.
- Social Media: Today, social media is being used as an essential tool to promote new articles or readings. Social media is a great way to share new content with followers and engage in comments and feedback. Consistent posting of related content and updates for new articles or blogs about the business helps tremendously. Other than SEO, social media activity is to update the fans and followers of the business.
- Business Information: Providing real business information enables Google to suggest the business because it indicates the necessary information that Google needs than retrieving such information via crawlers.
How does Robots.txt Commands Affect PageRank?
Robots.txt file contains notes of code identified by web crawlers to indicate specific instructions not to crawl on some pages that are not necessary. These pages are somehow the author does not want anyone to discover the page’s existence. Some web pages do not need Robots.txt file, but some do because of these instances; To block non-public pages for those containing the directory files that are not relevant for searches; Crawler budget problem focuses more on pages that matter; To prevent multimedia indexing such as PDF files. It prevents the miscalculation of PageRank due to non-public pages. Text information tells the search engine crawlers what URL or website links within the page the crawler access, according to the Robots.txt definition.
How does Nofollow Hint Affect PageRank?
Nofollow attribute was introduced by Google in 2005; it prevents incoming links to a website. Some advice that using the Nofollow attribute rel=”nofollow” tag, hurt the performance of the website, but according to Matt Cutts, it does not hurt the web page, as a direct answer. According to Nofollow definition, the attribute enables developers to block spammy incoming links. There are arguments on the internet since Nofollow is introduced to SEO developers, they said that Nofollow affects the PageRank of a page if implemented. There is no evidence to support the theory, but in perspective, owning a business is just like web pages; protecting the reputation against spammy schemes maintains a clean rank on the web. Applying the Nofollow attribute helps Google in identifying low-quality sites that are endorsing spam links. Nofollow attribute is not going to influence the destination URL regarding SEO, since the PageRank is not going to be transferred.
How does User-generated Content Links Affect PageRank?
User Generated Content or UGC is an attribute announced by Google in 2019. The purpose of UGC is to block user-generated content within the website that is made through comments or feedback. The UG attribute declared as rel=”UGC” incorporates a syntax that is, “<a href=”http://www.testarticle.com/” rel=”ugc”>Link Name</a>.” Through the attribute, Google is going to be notified that the link within the website is not endorsed by them, else it is just user-generated content. The link generated by the user is not going to gain any endorsement from the website, where it was generated.
How does Sponsored Hint Affect PageRank?
The sponsored hint attribute was announced in 2019 by Google together with the UGC attribute to answer the unsupervised incoming links. Nofollow links were used to serve the purpose before the sponsored link attribute was announced. The Sponsored attribute is used for all sponsored links that are incoming links. Google clearly states that promotions are not able to generate PageRank value; instead, they help gain attention and generate followers.
How does Link Popularity Affect PageRank?
Link Popularity is misconfigured as PageRank and vice versa. PageRank is identified as a subset of Link Popularity, as it focuses on quantity and popular links. While Link Popularity adds a quality factor in determining the score of the popularity of a page. As an example; a sneaker website has a link to the donut shop website; when someone clicks that link from the sneaker website, the donut shop gains PageRank, but the Popularity link gains not that much; for instance compare links that come from a coffee shop; the Link Popularity have much higher than came from a sneaker shop.
What are the Other PageRank Related Algorithms?
Listed below are the other PageRank related algorithms.
- Hilltop Algorithm: The hilltop algorithm is one of the adopted algorithms that Google used in 2003. The relationship with “experts” and “authority” are key factors the algorithm is looking at. The algorithm sees expert pages linked to relevant expert pages, while the authority pages link to the expert pages. The importance of relevance and credibility of a page dictates the authority of the page. The purpose of the algorithm in the first place was to look for relevant documents by using a specific keyword topic in the news search. Hilltop algorithm is created by Krishna Bharat and George A. Mihăilă; they are both now working with Google since 2003.
- TrustRank Algorithm: The TrustRank algorithm helps Google to separate spam web pages from legitimate, useful pages. The algorithm helps in the identification of quality web pages because of PageRank’s limited feature to recognize spam websites. Zoltan Gyongyi and Hector Garcia-Molina of Stanford University and Jan Pedersen of Yahoo introduced the TrustRank algorithm, to fight against spammy websites. The algorithm later became the primary to Googe and Yahoo! To provide quality SERP.
- EigenTrust Algorithm: The EigenTrust algorithm is used for peer-to-peer reputation management for the network. Sep Kamvar, Mario Schlosser, and Hector Garcia-Molina developed the EigenTrust algorithm, intending to evaluate and reduce the number of illegitimate files on the network. The EigenTrust algorithm provides a global trust value based on the record of uploads for the peers. Peer-to-peer file-sharing networks are widely used for uploading and downloading files; the EigenTrust algorithm lessens downloads of files that are malicious based on the history of uploads.
- SimRank Algorithm: The SimRank algorithm is short for similarity rank, and aims to compare domains that have similar relationships. The purpose of SimRank algorithms is to identify websites that have similar characteristics, especially when the other similar domain referenced the other similar object. Citation that came from a similar web page provides have scored for PageRank; the instance is resolved through SimRank that identifies the similarity of the referencing web page.
- VisualRank Algorithm: The VisualRank algorithm’s purpose is to identify the rank of images based on the quality of their content. The VisualRank algorithm uses computer vision technology and LSH or locality-sensitive hashing. The VisualRank algorithm indexes are clustered in a chart according to their similarity together with all the other images; a technique based on the image metadata and text for retrieving results of the PageRank.
- Katz Centrality: The Katz centrality determines the centrality of a link within the network based on a theoretic graph. Leo Katz presented the Katz centrality in 1953; to determine the influence of two personalities based on degrees of relation. The network that connects the link through these direct neighbors is calculated as the Katz centrality relative influence of a link in the network.
Does Google still use PageRank Algorithm?
Yes, Google still uses a PageRank algorithm. They still use the PageRank system, but with additional features and using different kinds of signals that are useful and with stronger intuition regarding identifying a good website.
Does Social Media Links affect PageRank Algorithm?
Yes, social media links affect the PageRank algorithm. It does not influence directly, but collectively the PageRank score eventually improves because of the visitors that are dragged in. Not all visitors are redirected to a specific web page that is recommended from a social media stays on the website. There are some that seek more information, engage with the website, and subscribe to it. It really shows how the impact of social media influences the PageRank algorithm for Web pages. Over billions of users, every day using social media such as YouTube, Facebook, Twitter, and Instagram. There are new growing platforms like TikTok that most businesses take advantage of the opportunity to promote what they offer. The social components of PageRank, according to Mayer and Pasquinelli, are social relationships and attention economy. The connections or networks of interest are chains that revolve around society. It always matters as to where the trend of how the interests of individuals change in the future.
Why is PageRank important for SEO?
PageRank is important for SEO because businesses are based on how well it is noticed by potential customers. The SEO role is to optimize the page and be on the SERP or search engine results page. The PageRank score is the basis of SEO of how well it performs. PageRank is an indicator of how important the page is as a relevant source of quality content, and as for other web pages as well.
Is Link Juice connected to PageRank?
No, Link juice is not connected to PageRank. But PageRank is affected by the link juice through how the backlinks are used to reference a web page. Link juice is a slang term used to describe the links in the content. Referencing other web domain links is not just providing link juices; it provides factual information that is relevant between pages. Referencing links through rankings is an essential component of link juice that makes sense for the concept. A post that uses a link transfers a part of the ranking; SEO professionals have to be mindful of it.
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