How Websites Get Ranked by Who Links to Them

This patent describes a computer method for scoring web pages or other linked documents based on the importance of the pages that link to them, helping search engines find better results.

What it covers

The patent describes a computer method for scoring documents in a linked database, such as the World Wide Web. It starts by "obtaining a plurality of documents" (Claim 1). Then, it "assigns a score to each of the linked documents based on scores of the one or more linking documents" (Claim 1). This means if an important page links to your page, your page gets a higher score. The method can use various "weighting factors" for these linking documents, such as the number of links they have (Claim 2), how likely they are to be accessed (Claim 3), or even user preferences (Claim 7). Finally, it involves "processing the linked documents according to their scores" (Claim 1), which could mean ordering them in search results. For example, if a popular news site links to a small blog, the blog's score would increase because of the news site's high score.

What it doesn't cover

• —Does not cover ranking documents solely based on their content keywords without considering incoming links.
• —Does not cover ranking systems that only count the number of incoming links without considering the quality or score of those linking documents.
• —Does not cover ranking documents where the score is determined only by user clicks on the document itself, rather than the linking documents.
• —Does not cover ranking systems that manually assign scores to documents without a computer-implemented method.

The clever bit

The truly novel idea was to treat links as "votes" of importance, where a link from a more important page counts more than a link from a less important page. This recursive definition of importance, combined with the "random surfer" model (Claim 10's "random traversal"), allowed for an objective and scalable way to rank the vast and ever-changing web.

Why it matters

This patent is foundational to how modern search engines, particularly Google, determine the relevance and authority of web pages. The core idea, known as PageRank, was developed by Larry Page and Sergey Brin at Stanford. It dramatically improved search results by moving beyond simple keyword matching to evaluate the entire link structure of the web. This method helped Google become the dominant search engine by providing more useful and relevant results.

Real-world examples

• 1.Google Search (PageRank algorithm)
• 2.Academic citation indexing (e.g., impact factor calculations)
• 3.Social network influence scoring
• 4.Web crawling and indexing systems