In many real-world domains, data can naturally be represented as networks. This is the case of social networks, bibliographic networks, sensor networks and biological networks. Some dynamism often characterizes these networks as their structure (i.e., nodes and edges) continually evolves. Considering this dynamism is essential for analyzing these networks accurately. In this work, we propose LP-ROBIN, a novel method that exploits incremental embedding to capture the dynamism of the network structure and predicts new links, which can be used to suggest friends in social networks, or interactions in biological networks, just to cite some. Differently from the state-of-the-art methods, LP-ROBIN can work with mutable sets of nodes, i.e., new nodes may appear over time without being known in advance. After the arrival of new data, LP-ROBIN does not need to retrain the model from scratch, but learns the embeddings of the new nodes and links, and updates the latent representations of old ones, to reflect changes in the network structure for link prediction purposes. The experimental results show that LP-ROBIN achieves better performances, in terms of AUC and F1-score, and competitive running times with respect to baselines, static node embedding approaches and state-of-the-art methods which use dynamic node embedding.