Graph Neural Networks (GNNs) now power high-stakes applications from drug discovery to fraud detection, making it essential to understand why a model makes a given prediction. Among the many GNN explanation techniques proposed, methods based on Shapley values stand out for their theoretical rigor and consistently superior fidelity. Their adoption, however, has been limited by cost: Shapley values are exponential to compute exactly, and even sampling-based approximations require hundreds of thousands of GNN inferences, leaving Shapley-based explainers an order of magnitude slower than competing methods. In this talk, I will present two systems that close this gap. GNNShap (WWW 2024) redesigns Shapley sampling across all coalition sizes, parallelizes sampling on a single GPU, and batches GNN inference to deliver both higher fidelity and faster runtimes than prior work. DistShap (VLDB 2026) extends these ideas to distributed memory by partitioning subgraph sampling across processes, running GNN inference in parallel across GPUs, and solving the underlying weighted least-squares problem in a distributed fashion. Together, these results show that the Shapley value—long established as a principled tool in game theory and a workhorse of modern ML explainability—can now extend its success to graph neural networks at scale.