  We discuss a methodology to dynamically generate links among digital objects
by means of an unsupervised learning mechanism which analyzes user link
traversal patterns. We performed an experiment with a test bed of 150 complex
data objects, referred to as buckets. Each bucket manages its own content,
provides methods to interact with users and individually maintains a set of
links to other buckets. We demonstrate that buckets were capable of dynamically
adjusting their links to other buckets according to user link selections,
thereby generating a meaningful network of bucket relations. Our results
indicate such adaptive networks of linked buckets approximate the collective
link preferences of a community of user
