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Poster
Track: Posters
Paper Title:
Web Graph Similarity for Anomaly Detection (Poster)
Authors:
- Panagiotis Papadimitriou(Stanford University)
- Ali Dasdan(Yahoo! Inc.)
- Hector Garcia-Molina(Stanford University)
Abstract:
Web graphs are approximate snapshots of the web, created by search
engines. Their creation is an error-prone procedure that relies on the
availability of Internet nodes and the faultless operation of multiple
software and hardware units. Checking the validity of a web graph
requires a notion of graph similarity. Web graph similarity helps
measure the amount and significance of changes in consecutive web
graphs. These measurements 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. 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.
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