Link prediction via Supervised Dynamic Network Formation

Abstract

The link prediction is a classical problem for computer science and many other research fields. Existing link prediction methods mainly apply the link patterns for the network in order to predict future possible links. However, in a network generated by the human interaction, the links may not only relate to the observed datasets, but also are affected by the decisions of human. That is to say, this kind of networks are affected by the game processes between all the related individuals. This paper proposes a model based on the dynamic network formation to mimic the game processes, and uses this model to deal with the link prediction problem. Moreover, we theoretically demonstrate the relationship between the parameters and the limit status of our model. Experimental results illustrate that the proposed model does better link prediction on the networks generated by human interaction than the traditional methods. Further more, this model gives a good prediction for the future possible links in a continuous time period.