Efficient pagerank approximation via graph aggregation

We present a framework for approximating random-walk based probability distributions over Web pages using graph aggregation. We (1) partition the Web's graph into classes of quasi-equivalent vertices, (2) project the page-based random walk to be approximated onto those classes, and (3) compute the stationary probability distribution of the resulting class-based random walk. From this distribution we can quickly reconstruct a distribution on pages. In particular, our framework can approximate the well-known PageRank distribution by setting the classes according to the set of pages on each Web host. We experimented on a Web-graph containing

• Type: Conference
• Year: 2004
• Where: WWW
• Authors: Andrei Z. Broder, Ronny Lempel, Farzin Maghoul, Jan O. Pedersen
• Keywords: Class-based Random Walk, Internet Technology, Stationary Probability Distribution, Well-known Pagerank Distribution, WWW 2004