In this paper we address the problem of temporal link prediction, i.e., predicting the apparition of new links, in time-evolving networks. This problem appears in applications such as recommender systems, social network analysis or citation analysis. Link prediction in time-evolving networks is usually based on the topological structure of the network only. We propose here a model which exploits multiple information sources in the network in order to predict link occurrence probabilities as a function of time. The model integrates three types of information: the global network structure, the content of nodes in the network if any, and the local or proximity information of a given vertex. The proposed model is based on a matrix factorization formulation of the problem with graph regularization. We derive an efficient optimization method to learn the latent factors of this model. Extensive experiments on several real world datasets suggest that our unified framework outperforms state-of-the-art methods for temporal link prediction tasks.