Web Graph Similarity for Anomaly Detection

Abstract

Web graphs are approximate snapshots of the web, created by search engines. They are essential to monitor the evolution of the web and to compute global properties like PageRank values of web pages. Their continuous monitoring requires a notion of graph similarity. Web graph similarity helps measure the amount and significance of changes in the evolving web. As a result, these measurements provide means to validate how well search engines acquire content from the web. In this paper we study five similarity schemes: three of them adapted from existing graph similarity measures, and two adapted from well-known document and vector similarity methods (namely, the shingling method and random projection based method). We compare and evaluate all five schemes using a sequence of web graphs for Yahoo!, and study if the schemes can identify anomalies that may occur due to hardware or other problems.

Available Versions of this Item

• Web Graph Similarity for Anomaly Detection. (deposited 21 Jan 2008 16:00) [Currently Displayed]
  • Web Graph Similarity for Anomaly Detection (poster). (deposited 06 May 2008 17:00)