# GNNavigator: Towards Adaptive Training of Graph Neural Networks via Automatic Guideline Exploration

arXiv: https://arxiv.org/abs/2404.09544 · HTML: https://mlsanity.com/paper/2404.09544/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 24 references that are in the map.
- First posted on arXiv 15 Apr 2024.

## Builds on (most cited first, 15 of 15)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (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)
- [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)
- [GNNAdvisor: An Adaptive and Efficient Runtime System for GNN Acceleration on GPUs](https://mlsanity.com/paper/2006.06608/) (2020)
- [BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessing](https://mlsanity.com/paper/2112.08541/) (2021)
- [Accelerating Training and Inference of Graph Neural Networks with Fast Sampling and Pipelining](https://mlsanity.com/paper/2110.08450/) (2021)
- [GCoD: Graph Convolutional Network Acceleration via Dedicated Algorithm and Accelerator Co-Design](https://mlsanity.com/paper/2112.11594/) (2021)
- [Hardware-Aware Graph Neural Network Automated Design for Edge Computing Platforms](https://mlsanity.com/paper/2303.10875/) (2023)
- [HP-GNN: Generating High Throughput GNN Training Implementation on CPU-FPGA Heterogeneous Platform](https://mlsanity.com/paper/2112.11684/) (2021)
- [GNNSampler: Bridging the Gap between Sampling Algorithms of GNN and Hardware](https://mlsanity.com/paper/2108.11571/) (2021)
- [Architectural Implications of GNN Aggregation Programming Abstractions](https://mlsanity.com/paper/2310.12184/) (2023)
- [HitGNN: High-throughput GNN Training Framework on CPU+Multi-FPGA Heterogeneous Platform](https://mlsanity.com/paper/2303.01568/) (2023)

## Built on by (most cited first, 2 of 2)

- [HGNAS: Hardware-Aware Graph Neural Architecture Search for Edge Devices](https://mlsanity.com/paper/2408.12840/) (2024)
- [MegaGraph: Towards Efficient Training of Large-Scale Graph Transformers with Automated Hybrid Parallelism](https://mlsanity.com/paper/2609.34420/) (2026)

## Abstract (arXiv)

Graph Neural Networks (GNNs) succeed significantly in many applications recently. However, balancing GNNs training runtime cost, memory consumption, and attainable accuracy for various applications is non-trivial. Previous training methodologies suffer from inferior adaptability and lack a unified training optimization solution. To address the problem, this work proposes GNNavigator, an adaptive GNN training configuration optimization framework. GNNavigator meets diverse GNN application requirements due to our unified software-hardware co-abstraction, proposed GNNs training performance model, and practical design space exploration solution. Experimental results show that GNNavigator can achieve up to 3.1x speedup and 44.9% peak memory reduction with comparable accuracy to state-of-the-art approaches.