Optimization of Graph Neural Networks Training Using Graph Reordering
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it