Incremental page rank computation on evolving graphs

Link Analysis has been a popular and widely used Web mining technique, especially in the area of Web search. Various ranking schemes based on link analysis have been proposed, of which the PageRank metric has gained the most popularity with the success of Google. Over the last few years, there has been significant work in improving the relevance model of PageRank to address issues such as personalization and topic relevance. In addition, a variety of ideas have been proposed to address the computational aspects of PageRank, both in terms of efficient I/O computations and matrix computations involved in computing the PageRank score. The key challenge has been to perform computation on very large Web graphs. In this paper, we propose a method to incrementally compute PageRank for a large graph that is evolving. We note that although the Web graph evolves over time, its rate of change is rather slow. When compared to its size. We exploit the underlying principle of first order markov mod...

• Type: Conference
• Year: 2005
• Where: WWW
• Authors: Prasanna Kumar Desikan, Nishith Pathak, Jaideep Srivastava, Vipin Kumar
• Keywords: Internet Technology, PageRank Metric, PageRank Score, Web Graph, WWW 2005