In this work, we explore applying a link prediction approach to tag recommendation in broad folksonomies. The original idea of the approach is to mine the dynamic of the tagging activity in order to compute the most suitable tag for a given user and a given resource. The tagging history of each user is modeled by a temporal sequence of bipartite graphs linking tags to resources. Given a target user and a target resource, we first compute a set of similar users. The tagging history of the identified set of users is merged in one temporal sequence on bipartite graphs. The obtained sequence is used to learn a model of link prediction in bipartite graphs. The learned model is then applied to predict tags to be linked to the target resource and a list of top similar resources. We get hence several ranked lists tags, one list for each considered resource. These ranked lists are then merged, applying classical preference merging methods in order to obtain the final output: a list of ranked tags that will be recommended to the user. We show through experiments conducted on real datasets extracted for the CiteULike folksonomy the soundness of the proposed approach.