Optimization of Graph Neural Networks Training Using Graph Reordering
Bibliographic record
Abstract
Graph neural networks (GNNs) are specifically designed for graph-structured data and have gained significant attention. However, training GNN on large-scale graphs remains challenging due to the iterative aggregation of high-dimensional features and high computational complexity. Graph sparsity often leads to inefficient memory access and prolonged training times. To address these issues, we propose the Maximum Common Neighbor Graph Reordering (MaCoN-GR) algorithm, which optimizes the memory layout by reducing the physical distance between nodes and their neighbors. We first evaluate MaCoN-GR through its performance on Sparse Matrix-Vector Multiplication (SpMV), a core operation in GNN training. Experimental results demonstrate that MaCoN-GR achieves speedups ranging from 1.10× to 1.35×, indicating that our method effectively enhances memory access efficiency and reduces latency. Building upon this result, we further apply MaCoN-GR to accelerate GNN training. Within the Topology-Oriented Sampling (TOS) model, GraphSAGE, MaCoN-GR achieves up to 1.08× speedup on large-scale datasets, along with notable improvements in accuracy. In the Feature-Oriented Sampling (FOS) framework, we evaluate both MaCoN-GR and METIS partitioning on FOSGNN, achieving speedups between 1.12× and 2.34×, while also improving convergence and model accuracy. These results highlight the effectiveness of MaCoN-GR in optimizing both memory performance and GNN training efficiency.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".