Web Graph Clustering for Displays and Navigation of Cyberspace

This chapter presents a new approach to clustering graphs, and applies it to Web graph display and navigation. The proposed approach takes advantage of the linkage patterns of graphs, and utilizes an affinity function in conjunction with the k-nearest neighbor. This chapter uses Web graph clustering as an illustrative example, and offers a potentially more applicable method to mine structural information from data sets, with the hope of informing readers of another aspect of data mining and its applications. INTRODUCTION A graph is suitable for World Wide Web (WWW) navigation. Nodes in a graph can be used to represent URLs and edges between nodes represent links between URLs. We can look at the entire cyberspace of the WWW as one graph — a huge and dynamic growing graph. It is, however, impossible to display this huge graph on the computer screen. Most current research interests are moving towards using “site mapping” methods (Chen, 1997; Maarek & Shaul, 1997) in an attempt to find an effective way of constructing a structured geometrical map for a single Website (a local map). This can guide the user This chapter appears in the book, Web Mining: Applications a d Techniques, edited by Anthony Scime. Copyright © 2005, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. 701 E. Chocolate Avenue, Suite 200, Hershey PA 17033-1240, USA Tel: 717/533-8845; Fax 717/533-8661; URL-http://www.idea-group.com IDEA GROUP PUBLISHING