Low Rank Page Rank: A matlab course project in sparse matrix computation
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CPSC 517: Sparse Matrix Computation
Low Rank Page Rank: A course project in sparse matrix computation
Brock Hargreaves

The problem of Pagerank is a simple one to state: Given a collection of websites, how do we
rank them? The primary way of formulating this utilizes a transition matrix which relates how web pages interact with each other.

We investigate what the effect of a low rank approximation for the transition matrix has on the power method and an inner-outer iteration for solving the Pagerank problem.

The purpose of the low rank approximation is two fold: (1) to reduce memory requirements (2) to decrease computational time. We show that we see an improvement in storage requirements and a decrease in computational time if we discard the time it takes to perform the low rank approximation, however at the sacrifice of accuracy.



         doc: PDF's for project and presentation and associated papers
   utilities: Various tools use for solving the pagerank problem
    examples: Examples from project and presentation
innout-small: Code and data from: "An inner-outer iteration for computing 

Add the utilities folder to your MATLAB path and execute 
project_examples.m. Note that you will need to  make a small edit to 
the working directory string and example string if you want.


1. The utilities priorityqueue and bigraph may need to be recompiled to 
be compatible with your system. Simply run their demo/test files.

2. I wrote my own implementations for each of the algorithms. However,
without a lot optimization in mind. One should try using the algorithms
included in innout-small.