Scalable GNN Explanations with Distributed Shapley Values

With the growing adoption of graph neural networks (GNNs), explaining their predictions has become increasingly important. However, attributing predictions to specific edges or features remains computationally expensive. For example, classifying a node with 100 neighbors using a 3-layer GNN may involve identifying important edges from millions of candidate subgraphs. To address this challenge, we develop DistShap, a parallel algorithm that distributes Shapley value-based explanations across multiple GPUs. DistShap samples subgraphs in a distributed setting, executes GNN inference in parallel across GPUs, and solves a distributed least squares problem to compute edge importance scores. DistShap outperforms most existing GNN explanation methods in accuracy and is the first to scale to GNN models with millions of edges by using 128 GPUs.