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The web graph is a commonly-used network representation of the hyperlinkstructure of a website. A network of similar structure to the web graph, whichwe call the session graph has properties that reflect the browsing habits ofthe agents in the web server logs. In this paper, we apply session graphs tocompare the activity of humans against web robots or crawlers. Understandingthese properties will enable us to improve models of HTTP traffic, which can beused to predict and generate realistic traffic for testing and improving webserver efficiency, as well as devising new caching algorithms. We applylarge-scale network properties, such as the connectivity and degreedistribution of human and Web robot session graphs in order to identifycharacteristics of the traffic which would be useful for modeling web trafficand improving cache performance. We find that the empirical degreedistributions of session graphs for human and robot requests on one Web serverare best fit by different theoretical distributions, indicating at a differencein the processes which generate the traffic.

Contrasting Web Robot and Human Behaviors with Network Models