  We examine the properties of all HTTP requests generated by a thousand
undergraduates over a span of two months. Preserving user identity in the data
set allows us to discover novel properties of Web traffic that directly affect
models of hypertext navigation. We find that the popularity of Web sites -- the
number of users who contribute to their traffic -- lacks any intrinsic mean and
may be unbounded. Further, many aspects of the browsing behavior of individual
users can be approximated by log-normal distributions even though their
aggregate behavior is scale-free. Finally, we show that users' click streams
cannot be cleanly segmented into sessions using timeouts, affecting any attempt
to model hypertext navigation using statistics of individual sessions. We
propose a strictly logical definition of sessions based on browsing activity as
revealed by referrer URLs; a user may have several active sessions in their
click stream at any one time. We demonstrate that applying a timeout to these
logical sessions affects their statistics to a lesser extent than a purely
timeout-based mechanism.
