摘要
This paper introduces GPart, a scalable multilevel framework for graph partitioning that integrates GNN embeddings with efficient coarsening and refinement techniques. On the Titan23 benchmarks, GPart achieves a cut size reduction of 34.13% to 42.92% over METIS and improves cut size by 9.30% on selected DIMACS benchmarks compared to G-kway. Furthermore, experiments on the Titan23 benchmarks show that GPart reduces normalized memory usage by 24.6x compared to GAP and 12.4x compared to GenPart. Unlike existing GNN-based methods, which require large hidden layers and substantial memory, GPart's multilevel architecture reduces hidden layer sizes, significantly optimizing memory efficiency.