The k -dimensional Weisfeiler-Lehman ( k -WL) algorithm—developed as an efficient heuristic for testing if two graphs are isomorphic—is a fundamental kernel for node embedding in the emerging field of graph neural networks. Unfortunately, the k -WL algorithm has exponential storage requirements, limiting the size of graphs that can be handled. This work presents a novel k -WL scheme with a storage requirement orders of magnitude lower while maintaining the same accuracy as the original k -WL algorithm. Due to the reduced storage requirement, our scheme allows for processing much bigger graphs than previously possible on a single compute node. For even bigger graphs, we provide the first distributed-memory implementation. Our k -WL scheme also has significantly reduced communication volume and offers high scalability. Our experimental results demonstrate that our approach is significantly faster and has superior scalability compared to five other implementations employing state-of-the-art techniques.