# GraphPAS: Parallel Architecture Search for Graph Neural Networks

arXiv: https://arxiv.org/abs/2112.03461 · HTML: https://mlsanity.com/paper/2112.03461/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 14 references that are in the map.
- First posted on arXiv 7 Dec 2021.

## Builds on (most cited first, 11 of 11)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [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)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [Position-aware Graph Neural Networks](https://mlsanity.com/paper/1906.04817/) (2019)
- [Large-Scale Learnable Graph Convolutional Networks](https://mlsanity.com/paper/1808.03965/) (2018)
- [Auto-GNN: Neural Architecture Search of Graph Neural Networks](https://mlsanity.com/paper/1909.03184/) (2019)
- [TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation](https://mlsanity.com/paper/2005.02844/) (2020)
- [Label-Consistency based Graph Neural Networks for Semi-supervised Node Classification](https://mlsanity.com/paper/2007.13435/) (2020)
- [Evolutionary Architecture Search for Graph Neural Networks](https://mlsanity.com/paper/2009.10199/) (2020)

## Built on by (most cited first, 5 of 5)

- [NAS-Bench-Graph: Benchmarking Graph Neural Architecture Search](https://mlsanity.com/paper/2206.09166/) (2022)
- [Designing the Topology of Graph Neural Networks: A Novel Feature Fusion Perspective](https://mlsanity.com/paper/2112.14531/) (2021)
- [Automated Graph Machine Learning: Approaches, Libraries, Benchmarks and Directions](https://mlsanity.com/paper/2201.01288/) (2022)
- [Survey of Graph Neural Network for Internet of Things and NextG Networks](https://mlsanity.com/paper/2405.17309/) (2024)
- [Graph Property Prediction on Open Graph Benchmark: A Winning Solution by Graph Neural Architecture Search](https://mlsanity.com/paper/2207.06027/) (2022)

## Abstract (arXiv)

Graph neural architecture search has received a lot of attention as Graph Neural Networks (GNNs) has been successfully applied on the non-Euclidean data recently. However, exploring all possible GNNs architectures in the huge search space is too time-consuming or impossible for big graph data. In this paper, we propose a parallel graph architecture search (GraphPAS) framework for graph neural networks. In GraphPAS, we explore the search space in parallel by designing a sharing-based evolution learning, which can improve the search efficiency without losing the accuracy. Additionally, architecture information entropy is adopted dynamically for mutation selection probability, which can reduce space exploration. The experimental result shows that GraphPAS outperforms state-of-art models with efficiency and accuracy simultaneously.