Link prediction is a ubiqtuous task that can be applied to various real world scenarios including biomedical interaction networks, social networks and recommendation systems. The goal of link prediction is to learn from a graph to infer missing or previously unknown relationships. For instance, in a social network we may use link prediction to power a friendship recommendation system, or in the case of biological network data, link prediction might be used to infer possible relationships between drugs and diseases. However, previous work on link prediction generally focuses only on one particular problem setting: previous work generally assumes that link prediction is to be performed on a single dense graph, with at least 50% of the true edges observed during training. Bose and his co-authors investigate how to perform link prediction when only a sparse sample (less than 30%) of edges are available. The authors formulate link prediction as a few-shot learning problem and solve it via a multi-graph, meta-learning strategy. They experiment on 3 very different datasets and find that Meta-Graph has the strongest performance in the sparse data regime, acheiving new state of the art results on sparse graphs.