Algorithms & Models

PageRank Algorithm — How It Ranks Web Pages

PageRank measures the importance of web pages by treating links as votes — and links from important pages count for more.

Reviewed 2025 Updated only when the core methodology changes

What the PageRank Algorithm Is

PageRank is a link-based ranking algorithm originally developed by Larry Page and Sergey Brin in the late 1990s. It was designed to measure the relative importance of web pages based on how links connect them.

Rather than ranking pages by content alone, PageRank treats the web as a network and assigns higher importance to pages that receive links from other important pages.

PageRank became the foundational ranking system behind early Google Search and remains one of the most influential ideas in information ranking systems.

What PageRank Ranks

PageRank ranks individual web pages, not entire websites or domains.

Each page receives a numerical score representing its relative importance within a network of linked pages. Pages with higher scores are considered more authoritative within that network and are more likely to rank higher when other factors are equal.

Core Inputs Used by PageRank

PageRank relies on a small set of structural inputs:

  • Inbound links — links pointing to a page from other pages
  • Outbound links — the number of links leaving a page
  • Link structure — how pages connect across the network
  • Damping factor — a probability value modeling random navigation

PageRank does not evaluate:

  • content quality
  • keyword relevance
  • user behavior
  • freshness

Those aspects are handled by other ranking systems layered on top.

How PageRank Scores Are Calculated (High-Level)

At a conceptual level, PageRank models a “random surfer” moving through the web by clicking links.

The algorithm works as follows:

  1. Every page starts with an equal amount of ranking value.
  2. A page passes its value to the pages it links to.
  3. Pages linked from important pages receive more value.
  4. This process repeats until the values stabilize.

Mathematically, PageRank can be expressed as a recursive formula where each page’s score depends on the scores of pages linking to it, scaled by a damping factor.

You do not need to understand or implement the formula to grasp the concept: importance flows through links, and links from important pages matter more.

The Role of the Damping Factor

The damping factor represents the probability that a user continues clicking links rather than jumping to a random page.

In early implementations, this value was commonly set around 0.85, meaning there was an 85% chance the user followed a link and a 15% chance they jumped elsewhere.

The damping factor prevents infinite loops and ensures that all pages receive some minimal level of importance.

Update Frequency

Classic PageRank is not updated continuously.

Early versions were recalculated periodically across the entire web graph. Over time, Google stopped publishing visible PageRank scores and integrated link analysis into more complex ranking systems.

Today, PageRank concepts are applied through variations and approximations rather than a single, publicly exposed score.

Known Limitations and Criticisms

PageRank has several well-documented limitations:

  • Link manipulation — artificial link networks can inflate scores
  • Topic blindness — the algorithm does not understand relevance
  • New page disadvantage — new pages start with low authority
  • Popularity bias — already popular pages tend to remain popular

Because of these weaknesses, PageRank is rarely used alone in modern ranking systems.

Where PageRank Is Used

PageRank-style link analysis is used in:

  • Web search engines (as one signal among many)
  • Academic citation analysis
  • Network science and graph theory
  • Recommendation systems
  • Social network influence modeling

The core idea extends well beyond search engines.

Summary

PageRank introduced the idea that importance can be inferred from connections, not just content. This insight reshaped how information is ranked and organized at scale.

While modern systems are far more complex, PageRank remains a foundational concept for understanding ranking systems in networks.

References and Sources

  • Page, L., Brin, S., Motwani, R., & Winograd, T. The PageRank Citation Ranking: Bringing Order to the Web (Stanford University).
  • Wikipedia. PageRank.
  • Google. How Search Works (high-level overview of ranking signals).
  • Langville, A. N., & Meyer, C. D. Google’s PageRank and Beyond: The Science of Search Engine Rankings.