# Can Graph Reordering Speed Up Graph Neural Network Training? An Experimental Study

arXiv: https://arxiv.org/abs/2409.11129 · HTML: https://mlsanity.com/paper/2409.11129/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 78 references that are in the map.
- First posted on arXiv 17 Sep 2024.

## Builds on (most cited first, 28 of 28)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Open Graph Benchmark: Datasets for Machine Learning on Graphs](https://mlsanity.com/paper/2005.00687/) (2020)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [Graph Convolutional Neural Networks for Web-Scale Recommender Systems](https://mlsanity.com/paper/1806.01973/) (2018)
- [DropEdge: Towards Deep Graph Convolutional Networks on Node Classification](https://mlsanity.com/paper/1907.10903/) (2019)
- [GraphSAINT: Graph Sampling Based Inductive Learning Method](https://mlsanity.com/paper/1907.04931/) (2019)
- [FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling](https://mlsanity.com/paper/1801.10247/) (2018)
- [Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks](https://mlsanity.com/paper/1905.07953/) (2019)
- [Graph Convolutional Networks for Text Classification](https://mlsanity.com/paper/1809.05679/) (2018)
- [Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks](https://mlsanity.com/paper/1911.07323/) (2019)
- [AliGraph: A Comprehensive Graph Neural Network Platform](https://mlsanity.com/paper/1902.08730/) (2019)
- [DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs](https://mlsanity.com/paper/2010.05337/) (2020)
- [PinnerSage: Multi-Modal User Embedding Framework for Recommendations at Pinterest](https://mlsanity.com/paper/2007.03634/) (2020)
- [DistGNN: Scalable Distributed Training for Large-Scale Graph Neural Networks](https://mlsanity.com/paper/2104.06700/) (2021)
- [Sampling methods for efficient training of graph convolutional networks: A survey](https://mlsanity.com/paper/2103.05872/) (2021)
- [Accelerating Training and Inference of Graph Neural Networks with Fast Sampling and Pipelining](https://mlsanity.com/paper/2110.08450/) (2021)
- [Survey on Graph Neural Network Acceleration: An Algorithmic Perspective](https://mlsanity.com/paper/2202.04822/) (2022)
- [Fast Graph Attention Networks Using Effective Resistance Based Graph Sparsification](https://mlsanity.com/paper/2006.08796/) (2020)
- [Slim Graph: Practical Lossy Graph Compression for Approximate Graph Processing, Storage, and Analytics](https://mlsanity.com/paper/1912.08950/) (2019)
- [Comprehensive Evaluation of GNN Training Systems: A Data Management Perspective](https://mlsanity.com/paper/2311.13279/) (2023)
- [Hybrid Edge Partitioner: Partitioning Large Power-Law Graphs under Memory Constraints](https://mlsanity.com/paper/2103.12594/) (2021)
- [GraphStorm: all-in-one graph machine learning framework for industry applications](https://mlsanity.com/paper/2406.06022/) (2024)
- [Out-of-Core Edge Partitioning at Linear Run-Time](https://mlsanity.com/paper/2203.12721/) (2022)
- [DiskGNN: Bridging I/O Efficiency and Model Accuracy for Out-of-Core GNN Training](https://mlsanity.com/paper/2405.05231/) (2024)
- [Window-based Streaming Graph Partitioning Algorithm](https://mlsanity.com/paper/1902.01543/) (2019)
- [Partitioner Selection with EASE to Optimize Distributed Graph Processing](https://mlsanity.com/paper/2304.04976/) (2023)

## Built on by (most cited first, 4 of 4)

- [Efficient GNN Training Through Structure-Aware Randomized Mini-Batching](https://mlsanity.com/paper/2504.18082/) (2025)
- [PLASMA: A Layout-Aware Benchmark Reveals Memory Layout Matters for Graph-based ANNS on GPU](https://mlsanity.com/paper/2508.15436/) (2025)
- [Not All Neighbors Matter: Understanding the Impact of Graph Sparsification on GNN Pipelines](https://mlsanity.com/paper/2603.06952/) (2026)
- [On Efficient Scaling of GNNs via IO-Aware Layers Implementations](https://mlsanity.com/paper/2605.31500/) (2026)

## Abstract (arXiv)

Graph neural networks (GNNs) are a type of neural network capable of learning on graph-structured data. However, training GNNs on large-scale graphs is challenging due to iterative aggregations of high-dimensional features from neighboring vertices within sparse graph structures combined with neural network operations. The sparsity of graphs frequently results in suboptimal memory access patterns and longer training time. Graph reordering is an optimization strategy aiming to improve the graph data layout. It has shown to be effective to speed up graph analytics workloads, but its effect on the performance of GNN training has not been investigated yet. The generalization of reordering to GNN performance is nontrivial, as multiple aspects must be considered: GNN hyper-parameters such as the number of layers, the number of hidden dimensions, and the feature size used in the GNN model, neural network operations, large intermediate vertex states, and GPU acceleration. In our work, we close this gap by performing an empirical evaluation of 12 reordering strategies in two state-of-the-art GNN systems, PyTorch Geometric and Deep Graph Library. Our results show that graph reordering is effective in reducing training time for CPU- and GPU-based training, respectively. Further, we find that GNN hyper-parameters influence the effectiveness of reordering, that reordering metrics play an important role in selecting a reordering strategy, that lightweight reordering performs better for GPU-based than for CPU-based training, and that invested reordering time can in many cases be amortized.