  The past few years have witnessed the great success of a new family of
paradigms, so-called folksonomy, which allows users to freely associate tags to
resources and efficiently manage them. In order to uncover the underlying
structures and user behaviors in folksonomy, in this paper, we propose an
evolutionary hypergrah model to explain the emerging statistical properties.
The present model introduces a novel mechanism that one can not only assign
tags to resources, but also retrieve resources via collaborative tags. We then
compare the model with a real-world dataset: \emph{Del.icio.us}. Indeed, the
present model shows considerable agreement with the empirical data in following
aspects: power-law hyperdegree distributions, negtive correlation between
clustering coefficients and hyperdegrees, and small average distances.
Furthermore, the model indicates that most tagging behaviors are motivated by
labeling tags to resources, and tags play a significant role in effectively
retrieving interesting resources and making acquaintance with congenial
friends. The proposed model may shed some light on the in-depth understanding
of the structure and function of folksonomy.
