# Simplifying Architecture Search for Graph Neural Network

arXiv: https://arxiv.org/abs/2008.11652 · HTML: https://mlsanity.com/paper/2008.11652/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Aug 2020.

## Built on by (most cited first, 28 of 28)

- [Neural Architecture Search: Insights from 1000 Papers](https://mlsanity.com/paper/2301.08727/) (2023)
- [Search to aggregate neighborhood for graph neural network](https://mlsanity.com/paper/2104.06608/) (2021)
- [Automated Machine Learning on Graphs: A Survey](https://mlsanity.com/paper/2103.00742/) (2021)
- [Pooling Architecture Search for Graph Classification](https://mlsanity.com/paper/2108.10587/) (2021)
- [NAS-Bench-Graph: Benchmarking Graph Neural Architecture Search](https://mlsanity.com/paper/2206.09166/) (2022)
- [Auto-HeG: Automated Graph Neural Network on Heterophilic Graphs](https://mlsanity.com/paper/2302.12357/) (2023)
- [Designing the Topology of Graph Neural Networks: A Novel Feature Fusion Perspective](https://mlsanity.com/paper/2112.14531/) (2021)
- [Stable Prediction on Graphs with Agnostic Distribution Shift](https://mlsanity.com/paper/2110.03865/) (2021)
- [Automated Graph Machine Learning: Approaches, Libraries, Benchmarks and Directions](https://mlsanity.com/paper/2201.01288/) (2022)
- [FL-AGCNS: Federated Learning Framework for Automatic Graph Convolutional Network Search](https://mlsanity.com/paper/2104.04141/) (2021)
- [AutoAC: Towards Automated Attribute Completion for Heterogeneous Graph Neural Network](https://mlsanity.com/paper/2301.03049/) (2023)
- [Search to Capture Long-range Dependency with Stacking GNNs for Graph Classification](https://mlsanity.com/paper/2302.08671/) (2023)
- [Search For Deep Graph Neural Networks](https://mlsanity.com/paper/2109.10047/) (2021)
- [Automatic Relation-aware Graph Network Proliferation](https://mlsanity.com/paper/2205.15678/) (2022)
- [A Versatile Graph Learning Approach through LLM-based Agent](https://mlsanity.com/paper/2309.04565/) (2023)
- [Combinatorial Optimization with Automated Graph Neural Networks](https://mlsanity.com/paper/2406.02872/) (2024)
- [Automatic Graph Topology-Aware Transformer](https://mlsanity.com/paper/2405.19779/) (2024)
- [Learn Layer-wise Connections in Graph Neural Networks](https://mlsanity.com/paper/2112.13585/) (2021)
- [Knowledge-aware Evolutionary Graph Neural Architecture Search](https://mlsanity.com/paper/2411.17339/) (2024)
- [Graph Property Prediction on Open Graph Benchmark: A Winning Solution by Graph Neural Architecture Search](https://mlsanity.com/paper/2207.06027/) (2022)
- [Search to Pass Messages for Temporal Knowledge Graph Completion](https://mlsanity.com/paper/2210.16740/) (2022)
- [Enhancing Intra-class Information Extraction for Heterophilous Graphs: One Neural Architecture Search Approach](https://mlsanity.com/paper/2211.10990/) (2022)
- [AutoHEnsGNN: Winning Solution to AutoGraph Challenge for KDD Cup 2020](https://mlsanity.com/paper/2111.12952/) (2021)
- [A Graph Neural Architecture Search Approach for Identifying Bots in Social Media](https://mlsanity.com/paper/2411.16285/) (2024)
- [ABG-NAS: Adaptive Bayesian Genetic Neural Architecture Search for Graph Representation Learning](https://mlsanity.com/paper/2504.21254/) (2025)
- [GraTO: Graph Neural Network Framework Tackling Over-smoothing with Neural Architecture Search](https://mlsanity.com/paper/2208.09027/) (2022)
- [Explainable Graph Neural Architecture Search via Monte-Carlo Tree Search (Full version)](https://mlsanity.com/paper/2308.15734/) (2023)
- [Node-level Graph Neural Architecture Search Framework](https://mlsanity.com/paper/2610.09297/) (2026)

## 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.