Comments on: Using your laptop to compute PageRank for millions of webpages

By: Jorge Catumba Ruiz

Jorge Catumba Ruiz — Wed, 29 Feb 2012 01:26:46 +0000

Ok, thanks.

By: Michael Nielsen

Michael Nielsen — Mon, 27 Feb 2012 19:42:26 +0000

Hi Jorge – Two things:

(1) My code was for educational purposes, and is not suited to production use. E.g., there are edge cases not dealt with, the data structures are deliberately chosen for clarity in exposition, not for utility in execution, and so on. I would not release the code for production use; frankly, it would need to be rewritten from scratch.

(2) The patent for PageRank is held by Google, and I’m not sure whether Sage could incorporate it in any case.

So I’m afraid I’d prefer not.

By: Jorge Catumba Ruiz

Jorge Catumba Ruiz — Sun, 26 Feb 2012 00:30:56 +0000

Hi, I want to use your code for implement the PageRank method in Sage with other methods similar to it. Have I your permission? If yes, I will put your name as author.

Thanks in advance!

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Fri, 29 Apr 2011 10:48:13 +0000

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By: Quora

Quora — Thu, 24 Feb 2011 09:34:20 +0000

What are some good resources for learning about matrix computations?…

This list is a stub. You can help by expanding it. * BellKor, Matrix factorization for recommender systems: www2.research.att.com/~volinsky/papers/ieeecomputer.pdf * BellKor, Scalable Collaborative Filtering..: public.research.att.com/~volinsky/netflix…

By: Data Scientists and Artists are the new upper class – and will be in the future « The Apps Lifestyle

Sun, 31 Oct 2010 20:38:17 +0000

[...] Linear algebra: a grounding in linear algebra is common among many data scientists, and important because matrix math underpins many data mining applications, such as the famous PageRank. [...]

By: links for 2010-05-27 « Blarney Fellow

links for 2010-05-27 « Blarney Fellow — Fri, 28 May 2010 01:34:46 +0000

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By: Michael Nielsen » Using your laptop to compute PageRank for millions of webpages : Popular Links : eConsultant

Thu, 27 May 2010 08:35:56 +0000

By: Michael Nielsen

Michael Nielsen — Wed, 30 Sep 2009 21:06:03 +0000

Daniel – Thanks for the note. When preparing the notes I considered doing something similar, but decided the presentation would be a little easier to understand without it. It’s been a while, but as I recall, my line of thought was simply that I wanted to avoid numpy, probably because the notes were originally prepared for people not at all familiar with numpy. The purpose of the notes is, of course, pedagogical – production code would be vastly different.

By: Daniel Smilkov

Daniel Smilkov — Wed, 30 Sep 2009 18:55:29 +0000

You know,

instead of using column matrices, you can use n-dim arrays:
v = numpy.matrix(numpy.zeros((n,1)))
becomes
v = numpy.zeros(n,dtype=numpy.float32)

and
p = numpy.matrix(numpy.ones((n,1)))/n
becomes
p = numpy.ones(n,dtype=numpy.float32)/n

and putting the initialization of the v vector out of step function, initializing it only ones in pagerank function,

you can get a lot of speed-up.