27. Eigenvalues and Eigenvectors in PageRank Algorithm

The PageRank algorithm, developed by Google, ranks webpages using eigenvalues and eigenvectors to determine their importance in a web graph.

This video provides a detailed explanation with a worked example, illustrating how the principal eigenvector of the Google Matrix assigns steady-state probabilities to webpages.

Learn how linear algebra powers search engines and why eigenvalues play a crucial role in ranking web content. Don't forget to like, share, and subscribe for more math insights!

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Видео 27. Eigenvalues and Eigenvectors in PageRank Algorithm канала Emmanuel Jesuyon Dansu