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Google: Exploring Computational Thinking

This paper serves as a companion or extension to the "Inside PageRank" paper by Bianchini et al. [19]. It is a comprehensive survey of all issues associated with PageRank, covering the basic PageRank model, available and recommended solution methods, storage issues, existence, uniqueness, and convergence properties, possible alterations to the basic model, suggested alternatives to the traditional solution methods, sensitivity and conditioning, and finally the updating problem. We introduce a few new results, provide an extensive reference list, and speculate about exciting areas of future research.

"We worked with artists to create unique works of art that visualize collaboration and the great ideas that happen when we build something together. We're calling it The Canvas Project."

I loved (and still love) the Shell boxes Thank you Malcolm RIP @EmathsUK #UKEdResChat #mathscpdchat https://t.co/zcVbnPwH2n