COMPARATIVE ANALYSIS OF DISTRIBUTED METHODS FOR PAGERANK COMPUTATION
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2018
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Haverford College. Department of Computer Science
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Thesis
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eng
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Open Access
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Abstract
The PageRank of a vector is a measure of centrality for nodes in a network. Three ways to calculate the PageRank are using power iteration, Monte Carlo methods, and iterative graph methods using Spark. The graph methods built in to spark and the power iteration method demonstrated a small difference in their performance when increasing the number of machines in a cluster. Running on AWS, the graph methods improved more when increasing the number of machines from 2 to 10 when compared to the power iteration methods. Experiments were carried out using Amazon Web Services and run on a subset of the web graph from Google. Improvements in partitioning could change these results, and could increase the performance of the matrix methods overall.