Gravity Effects on Information Filtering and Network Evolving

TL;DR

This paper introduces a gravity-inspired model for information filtering and network evolution, leveraging tag usage patterns to improve performance and better reflect real network properties.

Contribution

It proposes a novel tunable gravity-based model that incorporates tag usage to weigh network nodes, advancing understanding of network dynamics.

Findings

Enhanced algorithmic performance on real-world datasets

Better characterization of network properties

Potential insights into gravity model effects

Abstract

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.