documents one can track, it becomes increasingly difficult to remain
up to date on ones own areas of interest. The problem becomes more
severe with the increasing fraction of multimedia data, from which
it is difficult to extract some conceptual description of their
contents.
One way to overcome this problem are social bookmark tools, which
are rapidly emerging on the web. In such systems, users are setting
up lightweight conceptual structures called folksonomies, and
overcome thus the knowledge acquisition bottleneck. As more and more
people participate in the effort, the use of a common vocabulary
becomes more and more stable. We present an approach for discovering
topic-specific trends within folksonomies. It is based on a
differential adaptation of the PageRank algorithm to the triadic
hypergraph structure of a folksonomy. The approach allows for any
kind of data, as it does not rely on the internal structure of the
documents. In particular, this allows to consider different data
types in the same analysis step. We run experiments on a large-scale
real-world snapshot of a social bookmarking system. ER -