ScaleGNN uses communication-free sampling and 4D parallelism to scale mini-batch GNN training to 2048 GPUs, achieving 3.5x speedup over prior state-of-the-art on ogbn-products.
Adaptive sampling towards fast graph representation learning
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 2years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
A multi-granularity granular-ball coarsening algorithm reduces large graphs in linear time for faster GCN training on node classification, with experiments claiming superior performance over prior methods.
citing papers explorer
-
Communication-free Sampling and 4D Hybrid Parallelism for Scalable Mini-batch GNN Training
ScaleGNN uses communication-free sampling and 4D parallelism to scale mini-batch GNN training to 2048 GPUs, achieving 3.5x speedup over prior state-of-the-art on ogbn-products.
-
Efficient and Scalable Granular-ball Graph Coarsening Method for Large-scale Graph Node Classification
A multi-granularity granular-ball coarsening algorithm reduces large graphs in linear time for faster GCN training on node classification, with experiments claiming superior performance over prior methods.