mlsanityData to 7 Oct 2026

Simplifying Architecture Search for Graph Neural Network

arXiv 2008.11652 ↗First posted 26 Aug 2020cs.LG stat.MLcited by 27

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Abstract (arXiv)

Recent years have witnessed the popularity of Graph Neural Networks (GNN) in various scenarios. To obtain optimal data-specific GNN architectures, researchers turn to neural architecture search (NAS) methods, which have made impressive progress in discovering effective architectures in convolutional neural networks. Two preliminary works, GraphNAS and Auto-GNN, have made first attempt to apply NAS methods to GNN. Despite the promising results, there are several drawbacks in expressive capability and search efficiency of GraphNAS and Auto-GNN due to the designed search space. To overcome these drawbacks, we propose the SNAG framework (Simplified Neural Architecture search for Graph neural networks), consisting of a novel search space and a reinforcement learning based search algorithm. Extensive experiments on real-world datasets demonstrate the effectiveness of the SNAG framework compared to human-designed GNNs and NAS methods, including GraphNAS and Auto-GNN.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Aug 2020.

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Citing papers per year: 2021: 9; 2022: 7; 2023: 6; 2024: 4; 2025: 1.

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  1. 2023Neural Architecture Search: Insights from 1000 Paperscited by 136
  2. 2021Search to aggregate neighborhood for graph neural networkcited by 48
  3. 2021Automated Machine Learning on Graphs: A Surveycited by 38
  4. 2021Pooling Architecture Search for Graph Classificationcited by 36
  5. 2022NAS-Bench-Graph: Benchmarking Graph Neural Architecture Searchcited by 25
  6. 2023Auto-HeG: Automated Graph Neural Network on Heterophilic Graphscited by 23
  7. 2021Designing the Topology of Graph Neural Networks: A Novel Feature Fusion Perspectivecited by 19
  8. 2021Stable Prediction on Graphs with Agnostic Distribution Shiftcited by 9
  9. 2021FL-AGCNS: Federated Learning Framework for Automatic Graph Convolutional Network Searchcited by 8
  10. 2022Automated Graph Machine Learning: Approaches, Libraries, Benchmarks and Directionscited by 8
  11. 2023AutoAC: Towards Automated Attribute Completion for Heterogeneous Graph Neural Networkcited by 7
  12. 2023Search to Capture Long-range Dependency with Stacking GNNs for Graph Classificationcited by 7
15 more
  1. 2021Search For Deep Graph Neural Networkscited by 6
  2. 2022Automatic Relation-aware Graph Network Proliferationcited by 6
  3. 2023A Versatile Graph Learning Approach through LLM-based Agentcited by 6
  4. 2024Combinatorial Optimization with Automated Graph Neural Networkscited by 5
  5. 2024Automatic Graph Topology-Aware Transformercited by 4
  6. 2021Learn Layer-wise Connections in Graph Neural Networkscited by 3
  7. 2024Knowledge-aware Evolutionary Graph Neural Architecture Searchcited by 3
  8. 2022Graph Property Prediction on Open Graph Benchmark: A Winning Solution by Graph Neural Architecture Searchcited by 2
  9. 2022Search to Pass Messages for Temporal Knowledge Graph Completioncited by 2
  10. 2022Enhancing Intra-class Information Extraction for Heterophilous Graphs: One Neural Architecture Search Approachcited by 2
  11. 2021AutoHEnsGNN: Winning Solution to AutoGraph Challenge for KDD Cup 2020cited by 1
  12. 2024A Graph Neural Architecture Search Approach for Identifying Bots in Social Mediacited by 1
  13. 2025ABG-NAS: Adaptive Bayesian Genetic Neural Architecture Search for Graph Representation Learningcited by 1
  14. 2022GraTO: Graph Neural Network Framework Tackling Over-smoothing with Neural Architecture Searchcited by 0
  15. 2023Explainable Graph Neural Architecture Search via Monte-Carlo Tree Search (Full version)cited by 0