In this paper, based on the gravity principle of classical physics, wepropose a tunable gravity-based model, which considers tag usage pattern toweigh both the mass and distance of network nodes. We then apply this model insolving the problems of information filtering and network evolving.Experimental results on two real-world data sets, \emph{Del.icio.us} and\emph{MovieLens}, show that it can not only enhance the algorithmicperformance, but can also better characterize the properties of real networks.This work may shed some light on the in-depth understanding of the effect ofgravity model.
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