Mini-Batch Size Effects on GNN Inference Efficiency and Accuracy in Joint Structure-Label Estimation

This report synthesises findings from 1 peer-reviewed paper addressing the following research question: What is the impact of different mini-batch sizes on the inference efficiency (measured in FPS) and accuracy of graph neural networks when trained with joint structure-label estimation versus standard. Graph Neural Networks (GNNs) have emerged as a transformative paradigm in machine learning, enabling effective representation learning on non-Euclidean data structures such as social networks, citation graphs, protein-interaction networks, and knowledge bases. By integrating. 8 claims were extracted from source literature; 8 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.7/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: What is the impact of different mini-batch sizes on the inference efficiency (measured in FPS) and accuracy of graph neural networks when trained with joint structure-label estimation versus standard semi-supervised learning on the OGB-arXiv and OGB-mag benchmarks?

Autonomous literature synthesis. Automated review score: 8.7/10. Full text and citation available at Assignee Research.