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Low Rank Page Rank: A matlab course project in sparse matrix computation
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LRPR ==== 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. ----------------------------- Folders: 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 pagerank" 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. Notes: 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.