Linked from

The 21 pages that link to PageRank, each with the reason it gives.

• AlgorithmBroader topic: It illustrates how an iterative algorithm extracts rankings from the structure of the web.

• Markov chainBroader topic: Its random-surfer model turns web links into transitions between page states.

• Random walkBroader topic: A web-link graph becomes a walk whose long-run visit frequencies determine page scores.

• Fixed pointBroader topic: Its scores are obtained as a fixed point of a stochastic matrix transformation.

• GoogleBroader topic: PageRank helped distinguish Google’s early search results from competing search engines.

• EigenvectorBroader topic: Its ranking scores are obtained from a dominant eigenvector.

• Google SearchBroader topic: PageRank helped establish link structure as one signal for ranking web pages.

• Larry PageBroader topic: Page developed this link-analysis method with Brin as a foundation for Google’s search ranking.

• Sergey BrinBroader topic: The algorithm grew from Brin and Page’s work on BackRub.

• Matrix theoryBroader topic: Its defining computation finds an eigenvector of a stochastic matrix.

• GoogleBroader topic: PageRank helped distinguish Google Search by using the web’s link structure to rank pages.