DistShap: Scalable GNN Explanations with Distributed Shapley Values
We propose DistShap, a parallel algorithm that distributes Shapley value-based explanations of graph neural network predictions 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, scaling to GNN models with millions of features on up to 128 GPUs.