Abstract

Twitter is a very fast growing social networking site and has millions of users. Twitter user social relationship is based on follower concept rather that we are friend concept and following action is not mutual between Twitter user. Twitter users can be ranked using PageRank method as followers can be represented as social graph and the number of followers reflects influence propagation. In this dissertation we implemented Incremental PageRank using Hadoop MapReduce framework. We improved the existing Incremental PageRank method based on the idea that we can reduce the number of affected nodes that are descendants of changed nodes from going to recalculation stage by applying threshold restriction. We named our approach as Incremental+ method. Our experimental results show that the Incremental+ PageRank method is scalable because we successfully applied this method to calculate PageRank value for 1.47 billion Twitter following relations. Incremental+ method also produced the same ranking result as other methods even though we used approximation approach in calculating the PageRank value. The result also shows that Incremental+ method is efficient because it reduced the number of inputs per iteration and also reduced

Citations