EmbedPart achieves a 100x speedup over Metis for graph partitioning by clustering node embeddings rather than operating on raw graph structures.
April 2, 2026
Original Paper
EmbedPart: Embedding-Driven Graph Partitioning for Scalable Graph Neural Network Training
arXiv · 2604.01000
The Takeaway
Graph partitioning is the primary bottleneck in scaling GNNs to massive datasets. By shifting the problem to embedding space, this method maintains partitioning quality while drastically reducing the pre-processing time required for distributed training.
From the 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 p