[2604.01000] EmbedPart: Embedding-Driven Graph Partitioning for Scalable Graph Neural Network Training

Computer Science > Machine Learning arXiv:2604.01000 (cs) [Submitted on 1 Apr 2026] Title:EmbedPart: Embedding-Driven Graph Partitioning for Scalable Graph Neural Network Training Authors:Nikolai Merkel, Ruben Mayer, Volker Markl, Hans-Arno Jacobsen View a PDF of the paper titled EmbedPart: Embedding-Driven Graph Partitioning for Scalable Graph Neural Network Training, by Nikolai Merkel and 3 other authors View PDF HTML (experimental) Abstract:Graph Neural Networks (GNNs) are widely used for learning on graph-structured data, but scaling GNN training to massive graphs remains challenging. To enable scalable distributed training, graphs are divided into smaller partitions that are distributed across multiple machines such that inter-machine communication is minimized and computational load is balanced. In practice, existing partitioning approaches face a fundamental trade-off between partitioning overhead and partitioning quality. We propose EmbedPart, an embedding-driven partitioning approach that achieves both speed and quality. Instead of operating directly on irregular graph structures, EmbedPart leverages node embeddings produced during the actual GNN training workload and clusters these dense embeddings to derive a partitioning. EmbedPart achieves more than 100x speedup over Metis while maintaining competitive partitioning quality and accelerating distributed GNN training. Moreover, EmbedPart naturally supports graph updates and fast repartitioning, and can be applied...