Investigating Google's PageRank algorithm by

This paper presents different parallel implementations of Google’s PageRank algorithm. The purpose is to compare different methods for computing PageRank on large domains of the Web. The iterative algorithms used are the Power method and the Arnoldi method. We have implemented these algorithms in a parallel environment and created a basic Web-crawler to gather test data. Tests have then been carried out with the different algorithms using various test data. The explicitly restarted Arnoldi method was shown to be superior to the normal Arnoldi method as well as the Power method for high values of the dampening factor α. Results also show that load balancing our parallel implementation was usually quite ineffective. For smaller values of α, including 0.85 as Google uses, the Power method is preferable. It is usually somewhat slower, but the memory used is significantly less. For higher value