On Batch-size Selection for Stochastic Training for Graph Neural Networks
Bibliographic record
Abstract
Batch size is an important hyper-parameter for training deep learning models with stochastic gradient decent (SGD) method, and it has great influence on the training time and model performance. We study the batch size selection problem for training graph neural network (GNN) with SGD method. To reduce the training time while keeping a decent model performance, we propose a metric that combining both the variance of gradients and compute time for each mini-batch. We theoretically analyze how batch-size influence such a metric and propose the formula to evaluate some rough range of optimal batch size. In GNN, gradients evaluated on samples in a mini-batch are not independent and it is challenging to evaluate the exact variance of gradients. To address the dependency, we analyze an estimator for gradients that considers the randomness arising from two consecutive layers in GNN, and suggest a guideline for picking the appropriate scale of the batch size. We complement our theoretical results with extensive empirical experiments for ClusterGCN, FastGCN and GraphSAINT on 4 datasets: Ogbn-products, Ogbn-arxiv, Reddit and Pubmed. We demonstrate that in contrast to conventional deep learning models, GNNs benefit from large batch sizes.
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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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| 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".