Prevailing methods for graphs require abundant label and edge information for learning. However, labeled examples can be incredibly scarce in the case of the hardest and most impactful problems in science and medicine, such as novel drugs in development, emerging pathogens never seen before, and patients with rare diseases. In this talk, I describe our efforts to expand the scope and ease the applicability of graph representation learning for such challenging problems. First, I outline SubGNN, a subgraph neural network for learning disentangled subgraph embeddings. SubGNN generates embeddings that capture complex subgraph topology, including structure, neighborhood, and position of subgraphs. Second, I will introduce G-Meta, a theoretically justified meta-learning algorithm for graphs. G-Meta quickly adapts to a new task using only a handful of nodes or edges in the new task and does so by learning from local subgraphs in other graphs or related, albeit disjoint, label sets. Finally, I will discuss applications. The new methods successfully predicted treatments for an emerging disease, which were later experimentally confirmed in the wet laboratory. Further, the methods helped discover dozens of ultra high-order combinations of drugs safe for patients with considerably fewer unwanted side effects than today's treatments. Lastly, I describe our efforts in learning actionable representations that allow users to receive predictions that can be interpreted meaningfully.