Every SEO professional is familiar with PageRank.

PageRank is Google’s algorithm designed to measure the importance of a webpage based on the quality and quantity of webpages that refer to it. Pages that have higher PageRank (i.e., a large number of quality backlinks) are perceived as more authoritative by Google and rank better than those with lower PageRank.

For a long time, PageRank was a public metric — an SEO could download the Toolbar PageRank Chrome extension and estimate the importance of their webpages. However, in 2016, Google removed the extension from its browser and made the score unavailable, much to the dismay of SEO experts. Soon after this, many software providers began designing their own backlink-related indicators, providing the alternative to Google’s original PageRank score that SEOs can rely on.

*Using Backlinks API, you could also develop a backlink analysis tool with indicators closely related to PageRank.*

In its endpoints, you will discover various metrics estimating the authoritativeness of a page or domain based on its backlink profile. These metrics are `rank`

, `domain_from_rank`

, and `page_from_rank`

. They can become an acceptable alternative to PageRank, **as we use Google’s original PageRank formula when calculating them.**

`domain_from_rank`

displays the rank of a domain referring to the target page or domain specified in a POST request;`page_from_rank`

shows the rank of a page referring to the target page or domain specified in a POST request.

**The definition of the rank depends on the specific endpoint**

Endpoint | `Rank` definition |

Summary, Bulk Ranks | Rank of the target specified in the request |

History | Rank of the specified target on a specific date |

Backlinks | Rank that a given backlink passes to the target |

Anchors | Rank that referring websites pass on to the target through the links with a certain anchor |

Domain Pages, Domain Pages Summary | Rank of the specific page of the target website |

Referring Domains | Rank that a referring website passes on to the target |

Referring Networks | Rank that a referring network passes on to the target |

Competitors | Rank of the competitor domain |

Domain Intersection | Rank of the target |

Page Intersection | Rank that a certain backlink passes to the target |

Timeseries Summary | Rank of the target on a given date |

The ranks range from 0 (no backlinks detected) to 1000 (large number of quality backlinks).

The metrics are calculated based on the original method for node ranking in a linked database — the core principle behind Google’s PageRank algorithm. The exact formula is provided below.

#### How is the Rank of a page calculated?

To calculate the `rank`

of a page, we use the original PageRank formula.

Here it is:

PR(A) = (1-d) + d (PR(T1)/C(T1) + … + PR(Tn)/C(Tn))

To understand what variables are used in it, we have to refer to Google’s explanation:

*We assume page A has pages T1…Tn which point to it (i.e., are citations). The parameter d is a damping factor which can be set between 0 and 1. We usually set d to 0.85. …Also C(A) is defined as the number of links going out of page A.*

In a nutshell, Google considers the following factors when calculating PageRank:

- The quantity of inbound linking pages;
- The number of outbound links on each linking page;
- The PageRank of each linking page;
- The damping factor, simulating the probability of a random user continuing to click on links as they browse the web (it is perceived to decrease with each link click).

**Note that** “PageRank can be calculated using a simple iterative algorithm and corresponds to the principal eigenvector of the normalized link matrix of the web.” It means that Google’s PageRank algorithm can calculate the PR of a page without knowing the definitive PageRank of the referring pages. This is because PageRank isn’t an absolute score, but rather a relative measure of a webpage’s quality compared to every other page in the linking database (or simply the web). You can learn more about the mathematics behind the algorithm in this article.

**Backlinks API uses the same formula and thus takes into account the identical factors when calculating rank.**

**However,** we always set a damping factor to 0.5

Having a rapidly growing backlink index with more than 121,000,000,000 pages allows us to calculate `rank`

virtually for any page on the web.

Assume this article has backlinks from 3 pages. We know that:

- The first page has a PR of 400 and 32 outbound links in total;
- The second page has a PR of 300 and 40 outbound links in total;
- The third page has a PR of 200 and 5 outbound links in total.

Let’s calculate the PR of this page using the above variables:

**PR of this page** = (1 – 0.5) + 0.5 (400 / 32 + 300 / 40 + 200 / 5), which would be 30.5. Then, we should convert the number to integer, which is 30. That would be the `rank`

of this page.

**Please note that** not all backlinks are equal. Depending on the types of backlinks, they may pass all of their PageRank or don’t pass it at all.

- A
**301 link**passes all of its PageRank to a destination page. For example, if the PR of Page A is 400 and this page is linked to Page B through 301 redirect, the PR of Page B will also be 400; - A
**302 link**passes the same amount of PageRank as a regular backlink (see the formula above); - a
**canonical URL**passes all of its PageRank to a destination page; - a
**link with the “nofollow” attribute**doesn’t pass PageRank; however, it is still considered as a backlink by our system; - a
**link with the “ugc” attribute**doesn’t pass PageRank; however, it is still considered as a backlink by our system; - a link with the
**“sponsored” attribute**doesn’t pass PageRank; however, it is still considered as a backlink by our system.

#### How is the Rank of a domain calculated?

The `rank`

of a domain is the normalized sum of PRs of its indexed pages. To provide it, the API first calculates the `rank`

of each page the domain has, sums the values up, and then computes the log of the `rank`

values to compress them to a narrow range on a logarithmic scale.

Each point on the scale is hence more difficult to achieve. For example, it is relatively easy to move from 100 to 101, but the 500 to 501 transition would require a lot more effort (and backlinks).

#### Domain Rank Scale

Rank Score | Description |

25-35 | this score is usually received by newly created domains with a small number of backlinks |

200-300 | this score is usually received by large domains with a big number of backlinks from authoritative pages |

500+ | this score is usually received by huge domains with a significant number of backlinks from authoritative pages; very few domains in our database have such rank scores |