An improved clustering approach for web graph visualization

A Web graph is a graph which represents relationships between related web pages in the cyberspace, where a node is a web page URL and an edge reveals a link between two pages. A Web graph is a huge and complexity graph with the expanding of the WWW. To use the web graph as a tool for web navigation, users normally just get a small part of the Web graph based on their interest area. It is always a challenge for reducing visual complexities for the web graph. We develop a content based similarity method for graph clustering. This method deals mainly with the web pages' content to put related information together. For web graph visualization and navigation, we propose an improved clustering approach to combining structural and content information for web graph visualization. And give some application examples of using our approach