Graph Neural Networks (GNNs) have proven to be a powerful tool for machine learning tasks on static graphs, such as node classification, link prediction, and graph classification. However, many real-world networks are dynamic in nature, with edges and node attributes changing over time. For this reason Temporal Graph Neural Networks have recently emerged as a promising research area for machine learning tasks over this kind of temporal networks. Nevertheless, the added complexity introduced by the temporal dimension exacerbates well-known issues of GNN-based algorithms, such as over-squashing and over-smoothing, while further limiting the scalability of these approaches. In particular, this work focuses on dynamic supervised link prediction.