mlsanityData to 7 Oct 2026

GraphPAS: Parallel Architecture Search for Graph Neural Networks

arXiv 2112.03461 ↗First posted 7 Dec 2021cs.LGcited by 5

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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.

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

Builds on

11 of 14 references.

References · 11

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Graph Attention Networkscited by 8,854
  3. 2017Inductive Representation Learning on Large Graphscited by 6,284
  4. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,414
  5. 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,085
  6. 2019Position-aware Graph Neural Networkscited by 203
  7. 2018Large-Scale Learnable Graph Convolutional Networkscited by 113
  8. 2019Auto-GNN: Neural Architecture Search of Graph Neural Networkscited by 73
  9. 2020TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendationcited by 33
  10. 2020Label-Consistency based Graph Neural Networks for Semi-supervised Node Classificationcited by 11
  11. 2020Evolutionary Architecture Search for Graph Neural Networkscited by 10

Built on by

Cited by year

Citing papers per year: 2021: 1; 2022: 3; 2023: 0; 2024: 1.

5 papers.

  1. 2022NAS-Bench-Graph: Benchmarking Graph Neural Architecture Searchcited by 25
  2. 2021Designing the Topology of Graph Neural Networks: A Novel Feature Fusion Perspectivecited by 19
  3. 2022Automated Graph Machine Learning: Approaches, Libraries, Benchmarks and Directionscited by 8
  4. 2024Survey of Graph Neural Network for Internet of Things and NextG Networkscited by 4
  5. 2022Graph Property Prediction on Open Graph Benchmark: A Winning Solution by Graph Neural Architecture Searchcited by 2