PageRank is a link-analysis algorithm that estimates the importance of pages from the links between them. In the classic model, links from important pages contribute more than links from less important ones, and each page divides its contribution among the pages it links to. Google says PageRank remains part of its link-analysis systems, but has evolved substantially; it is neither a public score nor the whole of Google’s ranking system.
What PageRank measures
Think of the web as a directed graph: pages are nodes, and links are arrows between them. PageRank estimates a page’s importance in that graph. It is recursive: a page’s score depends on the scores of pages linking to it, and those scores depend in turn on other links.
That makes PageRank different from simply counting backlinks. Ten links from obscure pages do not necessarily carry the same weight as a link from a well-connected page. In the classic model, a linking page also divides its contribution among its outgoing links.
The method was developed at Stanford by Larry Page and Sergey Brin. The name refers to web pages as well as to Page. Their early work treated links in a way that resembles citations: a reference from an important source can be a stronger signal than one from an obscure source. Stanford’s original search-engine explanation and the original PageRank paper describe the approach.
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How the classic PageRank model works
The model uses a “random surfer” thought experiment. Imagine someone who usually follows links from the page they are viewing, but occasionally jumps to another page at random. A page accumulates importance when the surfer is likely to reach it, especially through links from pages that already have substantial importance.
A common simplified formula is:
PR(A) = (1 − d) + d × [PR(T₁)/C(T₁) + PR(T₂)/C(T₂) + … + PR(Tₙ)/C(Tₙ)]
- PR(A) is the score of the page being calculated.
- T₁ … Tₙ are pages linking to A.
- PR(Tᵢ) is the score of a page that links to A.
- C(Tᵢ) is the number of outgoing links from that linking page.
- d is the probability of continuing by following a link; 1 − d represents the random-jump part of the model.
The classic educational example often uses d = 0.85. That is a conventional value for explaining the model, not confirmation that Google uses exactly 0.85 throughout current Search. Google Cloud’s PageRank graph-algorithm documentation describes the damping-factor model in graph-centrality terms.
A three-page example
Consider three pages with these links:
- A links to B and C.
- B links to C.
- C links to A.
For a teaching example, start each page at 1/3, set d to 0.85, and use the simplified formula with a baseline of (1 − d)/3 = 0.05 per page. These values illustrate the arithmetic; they are not Google scores.
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Rank #2
First iteration
A has one incoming link, from C. C has one outgoing link, so it contributes its entire score to A. A’s new score is 0.05 + 0.85 × 1/3 = about 0.333.
B receives a contribution from A, but A links to two pages and therefore divides its score between B and C. B’s new score is 0.05 + 0.85 × (1/3 ÷ 2) = about 0.192.
C receives a half-share from A and a full share from B, which links only to C. Its new score is 0.05 + 0.85 × [(1/3 ÷ 2) + 1/3] = about 0.475.
Repeating the calculation
The new scores become the inputs for another round. A page’s score therefore reflects not only who links to it, but also how much importance those sources have accumulated. Repeating the calculation makes the values settle toward a stable distribution.
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In a computational implementation, the usual outline is to initialize scores, calculate a new score for every page, and repeat until changes are below a chosen tolerance. The number of iterations depends on the graph, initialization, and stopping threshold; there is no universal iteration count to apply to Google’s production system. The Stanford Information Retrieval text explains PageRank as one component of a broader composite search score.
Why damping and dangling pages matter
Without the random-jump component, a surfer could be trapped in a closed loop of pages that link only to one another, or be unable to reach a disconnected part of the graph by following links. The damping factor models occasional jumps and helps prevent importance from being confined to such structures.
A page with no outgoing links is called a dangling node. In a literal link-following model it passes no score onward, which complicates the calculation. Implementations typically account for these pages through their transition or redistribution rules. The precise treatment depends on the implementation; the classic explanation should not be mistaken for a disclosure of Google’s current production details.
PageRank is not the same as backlinks or rankings
A backlink is an input to a link graph; PageRank is a score calculated from the graph. The basic model divides a source page’s contribution equally among its outgoing links, but that does not mean every visible link passes an equal, measurable amount of ranking value in modern Google Search. Link processing can involve more systems and considerations than the textbook formula.
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A large backlink count does not guarantee a high PageRank or better placement. The graph’s structure matters, as do whether links can be crawled and processed, the context and relevance of the destination, and whether a link is treated as manipulative or spammy. Final rankings also depend on the query and the wider ranking system.
Is PageRank still used, and can you see it?
Google’s current ranking-systems documentation says PageRank remains among its link-analysis systems and has evolved substantially from its original form. That supports neither “PageRank is dead” nor the claim that today’s Google uses the 1998 formula unchanged. Google does not publish an ordinary user’s current internal PageRank value.
Google once showed a public PageRank indicator through the Toolbar; that visible score was retired. It is distinct from the link-analysis systems Google says remain in use. The historical account of the public score’s removal is summarized by Ahrefs’ PageRank glossary; it should not be read as a complete official account of Google’s motives.
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SEO platforms offer their own metrics to summarize link profiles. They can be useful for comparisons within a tool, but they are not Google PageRank and do not reveal Google’s internal score.
| Metric or concept | What it represents | Google PageRank? |
|---|---|---|
| Google PageRank | Google’s internal link-analysis system | Yes; its current internal value is not publicly exposed |
| Backlink count | Links a tool has discovered pointing to a page or site | No |
| Ahrefs URL Rating (UR) or Domain Rating (DR) | Ahrefs’ proprietary page- or domain-level link metrics | No |
| Semrush Authority Score | Semrush’s proprietary authority estimate | No |
| Moz Page Authority or Domain Authority | Moz’s proprietary page- or domain-level estimates | No |
These measures use different indexes, formulas, and scales, so they are not interchangeable with one another or with Google’s system. Treat them as directional tools for research, not as a score Google assigns.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to apply PageRank concepts to SEO
Build a useful internal link structure
Internal links connect your own pages. They help readers move between related information and help search engines discover and understand the site’s structure. A sound approach is to:
- Link from genuinely relevant pages and use descriptive anchor text that makes the destination clear to readers.
- Make important pages reachable from appropriate, useful sections of the site.
- Look for valuable pages that are orphaned or poorly connected, then add links where they help the reader.
- Check that important links are crawlable and point to the intended canonical destination.
- Review redirects, duplicate URL variants, and navigation rendered with JavaScript when diagnosing discovery or link-structure problems.
Do not add links everywhere just to maximize link volume. Excessive or repetitive links can make a page harder to use, and template-wide links do not create unlimited value. Internal linking is an information-architecture and usability practice as well as a way of structuring a site’s link graph.
Earn external references, rather than manufacture them
External links are more defensible when they are editorial references to something genuinely useful. Examples include original research or data, practical tools, strong reference material, and resources that are meaningfully better or more current than an existing one. Promote that work to relevant audiences and organizations that may choose to cite it.
Do not treat the PageRank model as an exception to Google’s spam policies. Buying links to manipulate rankings, automated link networks, large-scale guest posts created primarily for links, excessive reciprocal exchanges, comment or forum spam, and low-quality directories built for ranking manipulation are not sound link-earning strategies. A tactic can generate links and still be manipulative.
How to measure progress without a PageRank score
- Start with Google Search Console. Use its performance and indexing data to understand Google impressions, clicks, queries, and which pages are indexed. It is free, but does not provide a PageRank number or a complete competitor backlink database. See Google Search Console.
- Crawl your site when you need structural answers. Look for orphaned pages, broken internal links, redirect chains, and patterns that leave important content difficult to reach.
- Use a backlink index for external-link questions. Third-party tools can help discover referring pages and compare competitor link profiles; their coverage and authority scores remain estimates.
- Judge outcomes by outcomes. Monitor impressions, clicks, qualified organic visits, conversions, and whether important pages are indexed—not an unavailable internal score.
For a small site, Search Console and a focused site crawl may be enough. A paid SEO suite makes more sense when you have a concrete need for competitor backlink research, large-scale crawling, rank tracking, or reporting.
Quick Recap
Common misconceptions
- “PageRank is just backlinks counted.” It is recursive: the source pages’ importance and their outgoing links matter.
- “PageRank is dead.” Google still names it among its link-analysis systems, while noting that it has evolved.
- “PageRank is Google’s whole algorithm.” It is one part of a much broader set of ranking systems.
- “The 0.85 damping factor is confirmed for current Google.” It is a familiar teaching value in the classic model, not a confirmed universal production setting.
- “A high authority score guarantees rankings.” Vendor metrics are not Google scores, and link importance alone does not determine a query’s results.
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