Gravity Effects on Information Filtering and Network Evolving
This work addresses information filtering and network evolution for users of social tagging and recommendation systems, but it appears incremental as it builds on existing gravity models with tunable parameters.
The authors tackled the problem of information filtering and network evolution by proposing a tunable gravity-based model that incorporates tag usage patterns to weigh node mass and distance. Experimental results on Del.icio.us and MovieLens datasets showed improved algorithmic performance and better characterization of real network properties.
In this paper, based on the gravity principle of classical physics, we propose a tunable gravity-based model, which considers tag usage pattern to weigh both the mass and distance of network nodes. We then apply this model in solving 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 algorithmic performance, but can also better characterize the properties of real networks. This work may shed some light on the in-depth understanding of the effect of gravity model.