Link building

Link building

In the field of search engine optimization (SEO), link building describes actions aimed at increasing the number and quality of inbound links to a webpage with the goal of increasing the search engine rankings of that page or website. Briefly, link building is the process of establishing relevant hyperlinks (usually called links) to a website from external sites. Link building can increase the number of high-quality links pointing to a website, in turn increasing the likelihood of the website ranking highly in search engine results. Link building is also a proven marketing tactic for increasing brand awareness.

Editorial link

Editorial links are the links not acquired from paying money, asking, trading or exchanging. These links are attracted because of the good content and marketing strategies of a website. These are the links that the website owner does not need to ask for as they are naturally given by other website owners.

Resource link

Resource links are a category of links, which can be either one-way or two-way, usually referenced as “Resources” or “Information” in navbars, but sometimes, especially in the early, less compartmentalized years of the Web, simply called “links”. Basically, they are hyperlinks to a website or a specific webpage containing content believed to be beneficial, useful and relevant to visitors of the site establishing the link.

In recent years, resource links have grown in importance because most major search engines have made it plain that—in Google’s words—”quantity, quality, and relevance of links count towards your rating”.

Search engines measure a website’s value and relevance by analyzing the links to the site from other websites. The resulting “link popularity” is a measure of the number and quality of links to a website. It is an integral part of a website’s ranking in search engines. Search engines examine each of the links to a particular website to determine its value. Although every link to a website is a vote in its favor, not all votes are counted equally. A website with similar subject matter to the website receiving the inbound link carries more weight than an unrelated site, and a well-regarded site (such as a university) has higher link quality than an unknown or disreputable website.

The text of links helps search engines categorize a website. The engines’ insistence on resource links being relevant and beneficial developed because many artificial link building methods were employed solely to spam search engines, i.e. to “fool” the engines’ algorithms into awarding the sites employing these unethical devices undeservedly high page ranks and/or return positions.

Despite Google cautioning site developers to avoid “‘free-for-all’ links, link popularity schemes, or submitting a site to thousands of search engines these are typically useless exercises that don’t affect the ranking a site in the results of the major search engines”, most[which?] major engines have deployed technology designed to “red flag” and potentially penalize sites employing such practices.

Acquired link

These are the links acquired by the website owner through payment or distribution. They are also known as organically obtained links. Such links include link advertisements, paid linking, article distribution, directory links and comments on forums, blogs and other interactive forms of social media.

Reciprocal link

A reciprocal link is a mutual link between two objects, commonly between two websites, to ensure mutual traffic. For example, Alice and Bob have websites. If Bob’s website links to Alice’s website and Alice’s website links to Bob’s website, the websites are reciprocally linked. Website owners often submit their sites to reciprocal link exchange directories in order to achieve higher rankings in the search engines. Reciprocal linking between websites is no longer an important part of the search engine optimization process. In 2005, with their Jagger 2 update, Google stopped giving credit to reciprocal links as it does not indicate genuine link popularity.

Forum signature linking

Forum signature linking is a technique used to build backlinks to a website. This is the process of using forum communities that allow outbound hyperlinks in a member’s signature. This can be a fast method to build up inbound links to a website’s SEO value.

Blog comments

Leaving a comment on a blog can result in a relevant do-follow link to the individual’s website. Most of the time, however, leaving a comment on a blog turns into a no-follow link, which are not counted by search engines, such as Google and Yahoo! Search. On the other hand, blog comments are clicked on by the readers of the blog if the comment is well-thought-out and pertains to the discussion of the post on the blog.

Directory link

Website directories are lists of links to websites which are sorted into categories. Website owners can submit their site to many of these directories. Some directories accept payment for listing in their directory while others are free.

Social bookmarking

Social bookmarking is a way of saving and categorizing web pages in a public location on the web. Because bookmarks have anchor text and are shared and stored publicly, they are scanned by search engine crawlers and have search engine optimization value.

Image linking

Image linking is a way of submitting images, such as infographics, to image directories and linking them back to a specific URL.

Black hat link building

In early incarnations, when Google’s algorithm relied on incoming links as an indicator of website success, Black Hat SEOs manipulated website rankings by creating link-building schemes, such as building subsidiary websites to send links to a primary website. With an abundance of incoming links, the prime website outranked many reputable sites. However, the conflicts of being devalued by major search engines while building links could be caused by web owners using other black hat strategies. Black hat link building refers explicitly to the process of acquiring as many links as possible with minimal effort.

The Penguin algorithm was created to eliminate this type of abuse. At the time, Google clarified its definition of a “bad” link: “Any links intended to manipulate a site’s ranking in Google search results may be considered part of a link scheme.”

With Penguin, it wasn’t the quantity of links that improved your site but the quality. Since then, Google’s web spam team has attempted to prevent the manipulation of their search results through link building. Major brands including J.C. Penney, BMW, Forbes,, and many others have received severe penalties to their search rankings for employing spammy and non-user friendly link building tactics.

In October 5, 2014 Google launched a new algorithm update Penguin 3.0 to penalize those sites who use black hat link building tactics to build unnatural links to manipulate search engines. The update affected 0.3% English Language queries all over the world.

Black hat SEO could also be referred to as Spamdexing, which utilizes other black SEO strategies and link building tactics. Some black hat link building strategies include getting unqualified links from and participating in Link farm, link schemes and Doorway page. Black Hat SEO could also refer to “negative SEO,” the practice of deliberately harming another website’s performance.

White hat link building

White hat link building strategies are those strategies that add value to end users, abide by Google’s term of service and produce good results that could be sustained for a long time. White hat link building strategies focus on producing high-quality as well as relevant links to the website. Although more difficult to acquire, white hat link building tactics are widely implemented by website owners because such kind of strategies are not only beneficial to their websites’ long-term developments but also good to the overall online environment.

Internal link

An internal link is a type of hyperlink on a webpage to another page or resource, such as an image or document, on the same website or domain. Hyperlinks are considered either “external” or “internal” depending on their target or destination. Generally, a link to a page outside the same domain or website is considered external, whereas one that points at another section of the same webpage or to another page of the same website or domain is considered internal.

However, these definitions become clouded when the same organization operates multiple domains functioning as a single web experience, e.g. when a secure commerce website is used for purchasing things displayed on a non-secure website. In these cases, links that are “external” by the above definition can conceivably be classified as “internal” for some purposes. Ultimately, an internal link points to a web page or resource in the same root directory.

Similarly, seemingly “internal” links are in fact “external” for many purposes, for example in the case of linking among subdomains of a main domain, which are not operated by the same person(s). For example, a blogging platform, such as WordPress, Blogger or Tumblr host thousands of different blogs on subdomains, which are entirely unrelated and the authors of which are generally unknown to each other. In these contexts one might view a link as “internal” only if it linked within the same blog, not to other blogs within the same domain.

Both internal and external links allow users of the website to navigate to another web page or resource. This is the basis or founding idea or principal behind the internet. That users can navigate from one resource to another by clicking on hyperlinks. Internal links help users navigate the same website, whereas external links take users to a different website.

Both internal and external links help users surf the internet as well as having Search engine optimization value. Internal linking allows for good website nagivation and structure and allows search engines to crawl or spider websites.


PageRank (PR) is an algorithm used by Google Search to rank web pages in their search engine results. PageRank was named after Larry Page, one of the founders of Google. PageRank is a way of measuring the importance of website pages. According to Google:
PageRank works by counting the number and quality of links to a page to determine a rough estimate of how important the website is. The underlying assumption is that more important websites are likely to receive more links from other websites.

Currently, PageRank is not the only algorithm used by Google to order search results, but it is the first algorithm that was used by the company, and it is the best known


Cartoon illustrating the basic principle of PageRank. The size of each face is proportional to the total size of the other faces which are pointing to it.
PageRank is a link analysis algorithm and it assigns a numerical weighting to each element of a hyperlinked set of documents, such as the World Wide Web, with the purpose of “measuring” its relative importance within the set. The algorithm may be applied to any collection of entities with reciprocal quotations and references. The numerical weight that it assigns to any given element E is referred to as the PageRank of E and denoted by {\displaystyle PR(E).} PR(E). Other factors like Author Rank can contribute to the importance of an entity.

A PageRank results from a mathematical algorithm based on the webgraph, created by all World Wide Web pages as nodes and hyperlinks as edges, taking into consideration authority hubs such as or The rank value indicates an importance of a particular page. A hyperlink to a page counts as a vote of support. The PageRank of a page is defined recursively and depends on the number and PageRank metric of all pages that link to it (“incoming links”). A page that is linked to by many pages with high PageRank receives a high rank itself.

Numerous academic papers concerning PageRank have been published since Page and Brin’s original paper. In practice, the PageRank concept may be vulnerable to manipulation. Research has been conducted into identifying falsely influenced PageRank rankings. The goal is to find an effective means of ignoring links from documents with falsely influenced PageRank.

Other link-based ranking algorithms for Web pages include the HITS algorithm invented by Jon Kleinberg (used by Teoma and now,the IBM CLEVER project, the TrustRank algorithm and the Hummingbird algorithm.

Generalization of PageRank and eigenvector centrality for ranking objects of two kinds

A generalization of PageRank for the case of ranking two interacting groups of objects was described in  In applications it may be necessary to model systems having objects of two kinds where a weighted relation is defined on object pairs. This leads to considering bipartite graphs. For such graphs two related positive or nonnegative irreducible matrices corresponding to vertex partition sets can be defined. One can compute rankings of objects in both groups as eigenvectors corresponding to the maximal positive eigenvalues of these matrices. Normed eigenvectors exist and are unique by the Perron or Perron-Frobenius theorem. Example: consumers and products. The relation weight is the product consumption rate.

Distributed algorithm for PageRank computation

There are simple and fast random walk-based distributed algorithms for computing PageRank of nodes in a network. They present a simple algorithm that takes {\displaystyle O(\log n/\epsilon )} O(\log n/\epsilon) rounds with high probability on any graph (directed or undirected), where n is the network size and {\displaystyle \epsilon } \epsilon is the reset probability ( {\displaystyle 1-\epsilon } 1-\epsilon is also called as damping factor) used in the PageRank computation. They also present a faster algorithm that takes {\displaystyle O({\sqrt {\log n}}/\epsilon )} O(\sqrt{\log n}/\epsilon) rounds in undirected graphs. Both of the above algorithms are scalable, as each node processes and sends only small (polylogarithmic in n, the network size) number of bits per round.

Google Toolbar

The Google Toolbar long had a PageRank feature which displayed a visited page’s PageRank as a whole number between 0 and 10. The most popular websites displayed a PageRank of 10. The least showed a PageRank of 0. Google has not disclosed the specific method for determining a Toolbar PageRank value, which is to be considered only a rough indication of the value of a website. In March 2016 Google announced it would no longer support this feature, and the underlying API would soon cease to operate.

SERP rank

The search engine results page (SERP) is the actual result returned by a search engine in response to a keyword query. The SERP consists of a list of links to web pages with associated text snippets. The SERP rank of a web page refers to the placement of the corresponding link on the SERP, where higher placement means higher SERP rank. The SERP rank of a web page is a function not only of its PageRank, but of a relatively large and continuously adjusted set of factors (over 200). Search engine optimization (SEO) is aimed at influencing the SERP rank for a website or a set of web pages.

Positioning of a webpage on Google SERPs for a keyword depends on relevance and reputation, also known as authority and popularity. PageRank is Google’s indication of its assessment of the reputation of a webpage: It is non-keyword specific. Google uses a combination of webpage and website authority to determine the overall authority of a webpage competing for a keyword. The PageRank of the HomePage of a website is the best indication Google offers for website authority.

After the introduction of Google Places into the mainstream organic SERP, numerous other factors in addition to PageRank affect ranking a business in Local Business Results.

Google directory PageRank

The Google Directory PageRank was an 8-unit measurement. Unlike the Google Toolbar, which shows a numeric PageRank value upon mouseover of the green bar, the Google Directory only displayed the bar, never the numeric values. Google Directory was closed on July 20, 2011.

False or spoofed PageRank

In the past, the PageRank shown in the Toolbar was easily manipulated. Redirection from one page to another, either via a HTTP 302 response or a “Refresh” meta tag, caused the source page to acquire the PageRank of the destination page. Hence, a new page with PR 0 and no incoming links could have acquired PR 10 by redirecting to the Google home page. This spoofing technique was a known vulnerability. Spoofing can generally be detected by performing a Google search for a source URL; if the URL of an entirely different site is displayed in the results, the latter URL may represent the destination of a redirection.

Manipulating PageRank

For search engine optimization purposes, some companies offer to sell high PageRank links to webmasters. As links from higher-PR pages are believed to be more valuable, they tend to be more expensive. It can be an effective and viable marketing strategy to buy link advertisements on content pages of quality and relevant sites to drive traffic and increase a webmaster’s link popularity. However, Google has publicly warned webmasters that if they are or were discovered to be selling links for the purpose of conferring PageRank and reputation, their links will be devalued (ignored in the calculation of other pages’ PageRanks). The practice of buying and selling links is intensely debated across the Webmaster community. Google advises webmasters to use the nofollow HTML attribute value on sponsored links. According to Matt Cutts, Google is concerned about webmasters who try to game the system, and thereby reduce the quality and relevance of Google search results.

Directed Surfer Model

A more intelligent surfer that probabilistically hops from page to page depending on the content of the pages and query terms the surfer that it is looking for. This model is based on a query-dependent PageRank score of a page which as the name suggests is also a function of query. When given a multiple-term query, Q={q1,q2,…}, the surfer selects a q according to some probability distribution, P(q) and uses that term to guide its behavior for a large number of steps. It then selects another term according to the distribution to determine its behavior, and so on. The resulting distribution over visited web pages is QD-PageRank.

Social components

The PageRank algorithm has major effects on society as it contains a social influence. As opposed to the scientific viewpoint of PageRank as an algorithm the humanities instead view it through a lens examining its social components. In these instances, it is dissected and reviewed not for its technological advancement in the field of search engines, but for its societal influences. Laura Granka discusses PageRank by describing how the pages are not simply ranked via popularity as they contain a reliability that gives them a trustworthy quality. This has led to a development of behavior that is directly linked to PageRank. PageRank is viewed as the definitive rank of products and businesses and thus, can manipulate thinking. The information that is available to individuals is what shapes thinking and ideology and PageRank is the device that displays this information. The results shown are the forum to which information is delivered to the public and these results have a societal impact as they will affect how a person thinks and acts.

Katja Mayer views PageRank as a social network as it connects differing viewpoints and thoughts in a single place. People go to PageRank for information and are flooded with citations of other authors who also have an opinion on the topic. This creates a social aspect where everything can be discussed and collected to provoke thinking. There is a social relationship that exists between PageRank and the people who use it as it is constantly adapting and changing to the shifts in modern society. Viewing the relationship between PageRank and the individual through sociometry allows for an in-depth look at the connection that results.

Matteo Pasquinelli reckons the basis for the belief that PageRank has a social component lies in the idea of attention economy. With attention economy, value is placed on products that receive a greater amount of human attention and the results at the top of the PageRank garner a larger amount of focus then those on subsequent pages. The outcomes with the higher PageRank will therefore enter the human consciousness to a larger extent. These ideas can influence decision-making and the actions of the viewer have a direct relation to the PageRank. They possess a higher potential to attract a user’s attention as their location increases the attention economy attached to the site. With this location they can receive more traffic and their online marketplace will have more purchases. The PageRank of these sites allow them to be trusted and they are able to parlay this trust into increased business.

Other uses

The mathematics of PageRank are entirely general and apply to any graph or network in any domain. Thus, PageRank is now regularly used in bibliometrics, social and information network analysis, and for link prediction and recommendation. It’s even used for systems analysis of road networks, as well as biology, chemistry, neuroscience, and physics.

In neuroscience, the PageRank of a neuron in a neural network has been found to correlate with its relative firing rate.

Personalized PageRank is used by Twitter to present users with other accounts they may wish to follow ceme online

Swiftype’s site search product builds a “PageRank that’s specific to individual websites” by looking at each website’s signals of importance and prioritizing content based on factors such as number of links from the home page.

A version of PageRank has recently been proposed as a replacement for the traditional Institute for Scientific Information (ISI) impact factor, and implemented at Eigenfactor as well as at SCImago. Instead of merely counting total citation to a journal, the “importance” of each citation is determined in a PageRank fashion.

A similar new use of PageRank is to rank academic doctoral programs based on their records of placing their graduates in faculty positions. In PageRank terms, academic departments link to each other by hiring their faculty from each other (and from themselves).

PageRank has been used to rank spaces or streets to predict how many people (pedestrians or vehicles) come to the individual spaces or streets. In lexical semantics it has been used to perform Word Sense Disambiguation, Semantic similarity, and also to automatically rank WordNet synsets according to how strongly they possess a given semantic property, such as positivity or negativity.

In sport the PageRank algorithm has been used to rank the performance of: teams in the National Football League (NFL) in the USA; individual soccer players; and athletes in the Diamond League.

A Web crawler may use PageRank as one of a number of importance metrics it uses to determine which URL to visit during a crawl of the web. One of the early working papers that were used in the creation of Google is Efficient crawling through URL ordering, which discusses the use of a number of different importance metrics to determine how deeply, and how much of a site Google will crawl. PageRank is presented as one of a number of these importance metrics, though there are others listed such as the number of inbound and outbound links for a URL, and the distance from the root directory on a site to the URL.

The PageRank may also be used as a methodology to measure the apparent impact of a community like the Blogosphere on the overall Web itself. This approach uses therefore the PageRank to measure the distribution of attention in reflection of the Scale-free network paradigm.[citation needed]

In any ecosystem, a modified version of PageRank may be used to determine species that are essential to the continuing health of the environment.

For the analysis of protein networks in biology PageRank is also a useful tool.

In 2005, in a pilot study in Pakistan, Structural Deep Democracy, SD2 was used for leadership selection in a sustainable agriculture group called Contact Youth. SD2 uses PageRank for the processing of the transitive proxy votes, with the additional constraints of mandating at least two initial proxies per voter, and all voters are proxy candidates. More complex variants can be built on top of SD2, such as adding specialist proxies and direct votes for specific issues, but SD2 as the underlying umbrella system, mandates that generalist proxies should always be used.

Pagerank has recently been used to quantify the scientific impact of researchers. The underlying citation and collaboration networks are used in conjunction with pagerank algorithm in order to come up with a ranking system for individual publications which propagates to individual authors. The new index known as pagerank-index (Pi) is demonstrated to be fairer compared to h-index in the context of many drawbacks exhibited by h-index.


In early 2005, Google implemented a new value, “nofollow”, for the rel attribute of HTML link and anchor elements, so that website developers and bloggers can make links that Google will not consider for the purposes of PageRank—they are links that no longer constitute a “vote” in the PageRank system. The nofollow relationship was added in an attempt to help combat spamdexing.

As an example, people could previously create many message-board posts with links to their website to artificially inflate their PageRank. With the nofollow value, message-board administrators can modify their code to automatically insert “rel=’nofollow'” to all hyperlinks in posts, thus preventing PageRank from being affected by those particular posts. This method of avoidance, however, also has various drawbacks, such as reducing the link value of legitimate comments. (See: Spam in blogs#nofollow)

In an effort to manually control the flow of PageRank among pages within a website, many webmasters practice what is known as PageRank Sculpting—which is the act of strategically placing the nofollow attribute on certain internal links of a website in order to funnel PageRank towards those pages the webmaster deemed most important. This tactic has been used since the inception of the nofollow attribute, but may no longer be effective since Google announced that blocking PageRank transfer with nofollow does not redirect that PageRank to other links.


PageRank was once available for the verified site maintainers through the Google Webmaster Tools interface. However, on October 15, 2009, a Google employee confirmed that the company had removed PageRank from its Webmaster Tools section, saying that “We’ve been telling people for a long time that they shouldn’t focus on PageRank so much. Many site owners seem to think it’s the most important metric for them to track, which is simply not true.” In addition, The PageRank indicator is not available in Google’s own Chrome browser.

The visible page rank is updated very infrequently. It was last updated in November 2013. In October 2014 Matt Cutts announced that another visible pagerank update would not be coming.

Even though “Toolbar” PageRank is less important for SEO purposes, the existence of back-links from more popular websites continues to push a webpage higher up in search rankings.

Google elaborated on the reasons for PageRank deprecation at Q&A #March and announced Links and Content as the Top Ranking Factors, RankBrain was announced as the #3 Ranking Factor in October 2015 so the Top 3 Factors are now confirmed officially by Google.

On April 15, 2016 Google has officially shut down their Google Toolbar PageRank Data to public. Google had declared their intention to remove the PageRank score from the Google toolbar several months earlier. Google will still be using PageRank score when determining how to rank content in search results.