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Abstract: The past few years have witnessed the great success of a new family ofparadigms, so-called folksonomy, which allows users to freely associate tags toresources and efficiently manage them. In order to uncover the underlyingstructures and user behaviors in folksonomy, in this paper, we propose anevolutionary hypergrah model to explain the emerging statistical properties.The present model introduces a novel mechanism that one can not only assigntags to resources, but also retrieve resources via collaborative tags. We thencompare the model with a real-world dataset: \emph{Del.icio.us}. Indeed, thepresent model shows considerable agreement with the empirical data in followingaspects: power-law hyperdegree distributions, negtive correlation betweenclustering coefficients and hyperdegrees, and small average distances.Furthermore, the model indicates that most tagging behaviors are motivated bylabeling tags to resources, and tags play a significant role in effectivelyretrieving interesting resources and making acquaintance with congenialfriends. The proposed model may shed some light on the in-depth understandingof the structure and function of folksonomy.

Author: Zi-Ke Zhang, Chuang Liu

Source: https://arxiv.org/

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