BibSonomy ::  concept ::

Intelligent Control and Automation is a scholarly open access, peer-reviewed, quarterly and fully refereed journal focusing on theories, methods and applications in intelligent control and automation. The goal of this journal is to provide a platform for scientists and academicians all over the world to promote, share and discuss various new issues and developments in different areas of intelligent control and automation.

The PageRank algorithm is a great way of using collective intelligence to determine the importance of a webpage. There’s a big problem, though, which is that PageRank is difficult to apply to the web as a whole, simply because the web contains so many webpages. While just a few lines of code can be used to implement PageRank on collections of a few thousand webpages, it’s trickier to compute PageRank for larger sets of pages. The underlying problem is that the most direct way to compute the PageRank of n webpages involves inverting an n \times n matrix. Even when n is just a few thousand, this means inverting a matrix containing millions or tens of millions of floating point numbers. This is possible on a typical personal computer, but it’s hard to go much further. In this post, I describe how to compute PageRank for collections containing millions of webpages. My little laptop easily coped with two million pages, using about 650 megabytes of RAM and a few hours of computation