PageRank

PageRank is Google's original link-analysis algorithm — what it is, the random surfer model and the formula, the Reasonable Surfer update, its history from the Toolbar to the 2024 API leak, why it's still a core ranking system, and what it means for your links and internal architecture.

First published: Jun 27, 2026 · Last updated: Jul 18, 2026 · Advanced
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PageRank is the recursive link-graph model Larry Page and Sergey Brin published at Stanford: a page's score depends on the score of the pages linking to it, and in the published model each page splits its score across its outbound links ("links are votes" is the simplified way to say that). The public Toolbar score died in 2016, but Google's ranking systems guide still lists PageRank as one of its core ranking systems, evolved from the original. Leak-based reporting on Google's 2024 API leak described at least seven internal PageRank-related fields, though Google hasn't confirmed what each one does; practitioners (including this author) believe it also factors into crawl budget and canonicalization, though Google hasn't documented that directly. The Reasonable Surfer patent describes a model where not all links are weighted equally, but that's a patent, not a confirmed current production weighting. Nofollow sculpting has been dead since 2009, and Google says 30x redirects no longer lose PageRank. There's no public score anymore; third-party metrics like Ahrefs' URL Rating are proxies, not PageRank itself.

TL;DR — PageRankPageRank is Google's original recursive link-graph algorithm: a page's score depends on the scores of the pages linking to it, and in the published model each page's score is split across its outbound links (the simplified version: links are weighted votes). Google says it's evolved since launch but still part of its core ranking systems. is a recursive graph model: a page’s score is the probability a “random surfer” lands on it by following links, and that score depends on the scores of the pages linking to it. In the published model, each page splits its PageRank across its outbound links, and the calculation runs iteratively until it converges. The original 1998 algorithm was replaced internally around 2006, the Reasonable Surfer patent describes a model where not all links pass equal value (a patent claim, not a confirmed current weighting), and the public Toolbar score died in 2016. Google still lists PageRank as a core ranking system; leak-based reporting on Google’s 2024 API leak described at least seven internal PageRank-related fields, and practitioners (this author included) believe PageRank also factors into crawl budgetThe number of URLs an engine will crawl in a timeframe. and canonicalizationHow search engines pick one canonical URL among duplicates and consolidate signals onto it. — though Google hasn’t documented that link directly. Nofollowrel=\"nofollow\" is a value of the HTML link rel attribute that tells search engines you don't vouch for a linked page and don't want to pass ranking signals to it. Since 2019–2020 Google treats it as a hint, not a directive — and it does not reliably block crawling or indexing. sculpting has been dead since 2009; Google says 30x redirectsA redirect sends browsers and crawlers from a requested URL to a different one. An HTTP redirect specifically is a 3xx status code paired with a Location header; meta refresh and JavaScript redirects achieve a similar navigation without being a 3xx response themselves. Permanent redirects (301/308) are Google's signal the target should be canonical; temporary ones (302/303/307) aren't. no longer lose PageRank.

What PageRank actually is

PageRank is a mathematical link-analysis model, not a public toolbar score or a complete account of modern ranking. Evidence for this claim Official or primary documentation supporting the adjacent article claim, with scope limited to the source's published description. Scope: No ranking guarantee or undisclosed system mechanics are inferred beyond the cited source. Confidence: high · Verified: Stanford: PageRank paper Google’s current documentation confirms continued use without disclosing live weights or per-page values. Evidence for this claim Official or primary documentation supporting the adjacent article claim, with scope limited to the source's published description. Scope: No ranking guarantee or undisclosed system mechanics are inferred beyond the cited source. Confidence: high · Verified: Google: Ranking systems guide

PageRank is a link-analysis algorithm Larry Page and Sergey Brin developed at Stanford between 1996 and 1998. The name is a double meaning — it’s named after Larry Page, and it ranks web pages. It treats the web as a graph and inbound hyperlinks as votes, then assigns every page a score representing the probability that a random surfer — someone clicking links at random, occasionally jumping to a random page — ends up on that page.

What follows is the published, historical model from the 1999 Stanford paper — it’s the model Google has confirmed evolved since (see “Does Google still use PageRank?” below), so treat the formula and the worked example as how the original public algorithm worked, not a live description of Google’s current internal computation.

The formula from the original paper:

PR(A) = (1-d)/N + d * (PR(B)/L(B) + PR(C)/L(C) + PR(D)/L(D) + …)
  • d — the damping factor, typically 0.85: the probability the surfer keeps clicking rather than jumping to a random page.
  • N — total number of pages.
  • L(x) — number of outbound links on page x.
  • PR(x) — the PageRank of page x.

A few things fall out of that formula. Your PageRank depends on the PageRank of the pages linking to you — which is why it’s recursive and has to be computed iteratively until the numbers converge. Each linking page divides its PageRank across all its outbound links, so every extra outbound link dilutes the share each link passes. And the damping factor — 0.85 — models a 15% chance at each step that the surfer abandons the chain and starts fresh, which is what keeps the math from collapsing into “rank sinks” (pages with no outbound links).

A simplified worked example (illustrative only — a toy graph to show the mechanics, not a real site or a Google computation): say pages B, C, and D each link only to page A, and each of B, C, and D has a PageRank of 1 with one outbound link apiece (so each passes its full score to A). With N = 4 and d = 0.85:

PR(A) = (1 - 0.85)/4 + 0.85 * (1/1 + 1/1 + 1/1) = 0.0375 + 2.55 = 2.5875

That single pass shows the mechanics — every linking page’s score, divided by its outbound-link count, feeds A’s score — but a real computation iterates this across every page on the web until the scores stop changing, which is what “converges” means below.

One nuance almost nobody mentions, and that I dug into in The Evolution Of Google PageRank: the formula as published in the original paper contains a mathematical error. The leading constant is written as (1-d) when it should be (1-d)/N — divided by the total number of pages — for the probabilities to actually sum to 1. It’s a small thing, but it’s a fun reminder that even foundational papers ship with bugs.

The original model assumed a random surfer clicking every link with equal probability. The Reasonable Surfer patent (filed 2004, granted 2010, updated 2016) describes a different approach: a surfer who is more likely to click some links than others, so those links would pass more PageRank. A patent describes what Google claimed as a method it could use — it isn’t proof of the exact weights or logic running in production today, and Google hasn’t published a current, itemized list of what “counts.” Treat the features below as the patent’s described possibilities, not verified production weights:

  • Link position — main content beats sidebar beats footer.
  • Anchor textAnchor text is the visible, clickable text of a hyperlink. It tells readers what they'll find on the other end and gives search engines context about the linked page. — longer, more informative, more relevant anchors.
  • Visual prominence — font size, styling, above- vs. below-the-fold.
  • Link target — topical alignment between source and destination.
  • Actual click data — how often users really click that link.

The practical takeaway, based on what the patent describes: a link in the body of a high-authority page is plausibly worth more than the same link stuffed in that page’s footer. Pursue editorial, in-content links — the kind a real person would actually click — as a reasonable hedge even without confirmed current weights.

A short history

  • 1996–1998 — developed at Stanford. (Worth noting for accuracy: Robin Li, later Baidu’s founder, patented a similar link-ranking idea, RankDex, in 1996.)
  • 1998 — patent filed (January 9, 1998); the paper follows.
  • ~2000 — the Google Toolbar exposes a public 0–10 score (logarithmic scale).
  • ~2006 — the original algorithm is replaced internally with a faster, less resource-intensive version.
  • 2009 — Google changes how nofollowrel=\"nofollow\" is a value of the HTML link rel attribute that tells search engines you don't vouch for a linked page and don't want to pass ranking signals to it. Since 2019–2020 Google treats it as a hint, not a directive — and it does not reliably block crawling or indexing. works, killing PageRank sculpting (see Myths).
  • 2013 — last public Toolbar PageRank update (December).
  • 2016 — Toolbar PageRank removed entirely; Gary Illyes also confirms 30x redirects no longer lose PageRank.
  • 2017 — Illyes confirms it’s still in use: “after 18 years we’re still using PageRank.”
  • 2019 — the original patent expires (September 24, 2019).
  • 2024 — the Google API leak reveals at least seven PageRank variants; the updated SEO Starter Guide confirms PageRank is still active.

Does Google still use PageRank? Yes.

This is the question people actually ask, and the evidence is unambiguous. Google’s ranking systems guide says PageRank is “one of our core ranking systems used when Google first launched” and that “it continues to be part of our core ranking systems.” Removing the Toolbar score in 2016 removed a display, not the algorithm.

In May 2024, an anonymous source leaked thousands of internal Google Search API documentation files; SparkToro published the original report and Search Engine Land followed with further analysis. Google never confirmed or explained the leaked field names — what follows is the industry’s interpretation of what those fields are likely to be, not a Google-published specification:

FieldLikely meaning (per leak analysis, not Google-confirmed)
rawPagerankThe unprocessed base value from the link graph
pageRank2An updated/modified version
pageRank_NS”Nearest seed” — distance from trusted seed sites; tied to document understanding; reportedly deprecated
firstCoveragePageRankThe PageRank when Google first discovered the page
ToolBarPageRankThe old public toolbar score — stored, not updated

The same leak coverage described a link quality tier system (low / medium / high) that reportedly determines whether PageRank and anchor signals flow through a link at all, with click data (NavBoost) feeding into which tier a link lands in — again, this is leak-based reporting, not something Google has documented directly. If accurate, a low-tier link may pass nothing. For my own read on the canonicalizationHow search engines pick one canonical URL among duplicates and consolidate signals onto it. side of this, see my canonicalization guide — that piece is practitioner analysis, not a Google specification either.

Beyond ranking — PageRank’s other jobs (practitioner analysis)

PageRank probably isn’t only a ranking input, but I want to be precise about what’s confirmed and what isn’t: Google’s own documentation doesn’t explicitly name PageRank as a crawl-budget or canonicalization input. What follows is practitioner analysis — mine, in The Evolution Of Google PageRank — inferred from Google’s crawl-demand and canonicalization documentation plus general statements about page importance, not a direct Google confirmation:

  • A likely crawl-budget factor. Google’s crawl-demand documentation ties crawlingCrawling is how search engines use automated bots (like Googlebot and Bingbot) to discover URLs and download pages. A page has to be crawlable to be indexed, but crawling on its own isn't a ranking factor. frequency to a page’s perceived importance/popularity in its indexStoring a crawled page in the search index so it can appear in results. Crawled is not the same as indexed — Google selects what to keep, and indexing isn't guaranteed., without naming PageRank specifically; the common practitioner reading is that more important pages — higher PageRank — get crawled more frequently. This connects to crawl budgetThe number of URLs an engine will crawl in a timeframe. and crawl frequencyCrawl frequency is how often a search engine comes back to re-fetch a page it already knows about. Popular pages that change often get refreshed many times a day; stable pages can go weeks or months between crawls — and you influence it indirectly, not by setting a dial. elsewhere in this cluster.
  • A plausible canonicalization signal. When Google picks a canonical from a cluster of duplicates, it weighs roughly 40 signals it hasn’t fully enumerated publicly; link authority is understood to be one input, and the practitioner inference is that higher-PageRank pages are more likely to be chosen as the canonical. That inference isn’t independently confirmed by Google.

In 2023, Gary Illyes said links are not in his top three ranking factors and “hasn’t been for some time.” At SERPconf 2024 he went further — “We need very few links to rank pages” — and then, as I noted from the room, immediately walked it back (“I definitely shouldn’t have said that”). John Mueller has similarly said the weight on links will drop off over time and that you shouldn’t over-focus on link counts.

The honest read: links matter less than they used to for well-established sites, but they’re still foundational — for establishing authority, for crawl priority, and for canonicalization. “Less decisive” is not “irrelevant.”

What this means for your SEO

  • Earn links from genuinely authoritative, on-topic pages. The recursive math means one link from a strong, relevant page beats many from weak ones.
  • Chase editorial, in-content links. Reasonable Surfer logic: pursue links a real user would actually click, in the body, not the footer.
  • Treat internal linkingAn internal link is a hyperlink from one page on a website to another page on the same website. Internal links help search engines discover your pages and pass ranking signals (PageRank and anchor-text context) between them. as PageRank plumbing. Your architecture decides how PageRank flows to your money pages — keep important pages well-linked and shallow.
  • Don’t sculpt with nofollow. It’s been broken since 2009 — nofollowed PageRank evaporates rather than redistributing.
  • Don’t fear 30x redirects. No PageRank loss since 2016.
  • Avoid link schemes. Low-quality links can be tiered “low” and pass no signal at all — wasted effort at best.

Where this fits

PageRank is why crawlingCrawling is how search engines use automated bots (like Googlebot and Bingbot) to discover URLs and download pages. A page has to be crawlable to be indexed, but crawling on its own isn't a ranking factor. and linking matter in the first place, which is why it sits in the crawling sub-cluster. For the discovery and fetch side, see crawling, crawl budget, and crawl frequencyCrawl frequency is how often a search engine comes back to re-fetch a page it already knows about. Popular pages that change often get refreshed many times a day; stable pages can go weeks or months between crawls — and you influence it indirectly, not by setting a dial.; for how it shapes which URL Google keeps, see canonicalization. For the broader picture, see How Search WorksSearch works in three stages — crawling, indexing, and serving (ranking). A page has to clear each one to appear in results: getting crawled doesn't mean you're indexed, and getting indexed doesn't mean you rank..

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