Web graphs are approximate snapshots of the web, created by search engines. Their creation is an error-prone proce-dure 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 no-tion of graph similarity. Web graph similarity helps measure the amount and signicance of changes in consecutive web graphs. These measurements validate how well search en-gines acquire content from the web. In this paper we study vfive 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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