# NAS-Bench-Graph: Benchmarking Graph Neural Architecture Search

arXiv: https://arxiv.org/abs/2206.09166 · HTML: https://mlsanity.com/paper/2206.09166/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 60 of its 104 references that are in the map.
- First posted on arXiv 18 Jun 2022.

## Builds on (most cited first, 30 of 60)

- [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)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [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)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [DARTS: Differentiable Architecture Search](https://mlsanity.com/paper/1806.09055/) (2018)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Neural Architecture Search with Reinforcement Learning](https://mlsanity.com/paper/1611.01578/) (2016)
- [Relational inductive biases, deep learning, and graph networks](https://mlsanity.com/paper/1806.01261/) (2018)
- [Pitfalls of Graph Neural Network Evaluation](https://mlsanity.com/paper/1811.05868/) (2018)
- [Revisiting Semi-Supervised Learning with Graph Embeddings](https://mlsanity.com/paper/1603.08861/) (2016)
- [Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks](https://mlsanity.com/paper/1810.02244/) (2018)
- [Representation Learning on Graphs with Jumping Knowledge Networks](https://mlsanity.com/paper/1806.03536/) (2018)
- [Heterogeneous Graph Attention Network](https://mlsanity.com/paper/1903.07293/) (2019)
- [Image-based Recommendations on Styles and Substitutes](https://mlsanity.com/paper/1506.04757/) (2015)
- [Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizon](https://mlsanity.com/paper/1811.06128/) (2018)
- [DeepGCNs: Can GCNs Go as Deep as CNNs?](https://mlsanity.com/paper/1904.03751/) (2019)
- [Graph Neural Networks in Recommender Systems: A Survey](https://mlsanity.com/paper/2011.02260/) (2020)
- [NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search](https://mlsanity.com/paper/2001.00326/) (2020)
- [Deep Learning on Graphs: A Survey](https://mlsanity.com/paper/1812.04202/) (2018)
- [Large-Scale Evolution of Image Classifiers](https://mlsanity.com/paper/1703.01041/) (2017)
- [NAS-Bench-101: Towards Reproducible Neural Architecture Search](https://mlsanity.com/paper/1902.09635/) (2019)
- [Graph Neural Network for Traffic Forecasting: A Survey](https://mlsanity.com/paper/2101.11174/) (2021)
- [Random Search and Reproducibility for Neural Architecture Search](https://mlsanity.com/paper/1902.07638/) (2019)
- [Graph Neural Networks with convolutional ARMA filters](https://mlsanity.com/paper/1901.01343/) (2019)
- [Design Space for Graph Neural Networks](https://mlsanity.com/paper/2011.08843/) (2020)

## Built on by (most cited first, 25 of 25)

- [Spectral Invariant Learning for Dynamic Graphs under Distribution Shifts](https://mlsanity.com/paper/2403.05026/) (2024)
- [Disentangled Condensation for Large-scale Graphs](https://mlsanity.com/paper/2401.12231/) (2024)
- [FlowerFormer: Empowering Neural Architecture Encoding using a Flow-aware Graph Transformer](https://mlsanity.com/paper/2403.12821/) (2024)
- [TimeRecipe: A Time-Series Forecasting Recipe via Benchmarking Module Level Effectiveness](https://mlsanity.com/paper/2506.06482/) (2025)
- [Do Not Train It: A Linear Neural Architecture Search of Graph Neural Networks](https://mlsanity.com/paper/2305.14065/) (2023)
- [Out-of-Distribution Generalized Dynamic Graph Neural Network for Human Albumin Prediction](https://mlsanity.com/paper/2311.15545/) (2023)
- [Unsupervised Graph Neural Architecture Search with Disentangled Self-supervision](https://mlsanity.com/paper/2403.05064/) (2024)
- [Bkd-FedGNN: A Benchmark for Classification Backdoor Attacks on Federated Graph Neural Network](https://mlsanity.com/paper/2306.10351/) (2023)
- [Automatic Graph Topology-Aware Transformer](https://mlsanity.com/paper/2405.19779/) (2024)
- [Knowledge-aware Evolutionary Graph Neural Architecture Search](https://mlsanity.com/paper/2411.17339/) (2024)
- [GLEMOS: Benchmark for Instantaneous Graph Learning Model Selection](https://mlsanity.com/paper/2404.01578/) (2024)
- [CARL: Causality-guided Architecture Representation Learning for an Interpretable Performance Predictor](https://mlsanity.com/paper/2506.04001/) (2025)
- [Bilevel Optimization for Neural Architecture Search](https://mlsanity.com/paper/2606.29582/) (2026)
- [LEMUR 2: Unlocking Neural Network Diversity for AI](https://mlsanity.com/paper/2607.06839/) (2026)
- [Graph Neural Architecture Search with GPT-4](https://mlsanity.com/paper/2310.01436/) (2023)
- [Proficient Graph Neural Network Design by Accumulating Knowledge on Large Language Models](https://mlsanity.com/paper/2408.06717/) (2024)
- [ABG-NAS: Adaptive Bayesian Genetic Neural Architecture Search for Graph Representation Learning](https://mlsanity.com/paper/2504.21254/) (2025)
- [Loss Functions for Predictor-based Neural Architecture Search](https://mlsanity.com/paper/2506.05869/) (2025)
- [Learning to Flow from Generative Pretext Tasks for Neural Architecture Encoding](https://mlsanity.com/paper/2510.18360/) (2025)
- [Explainable Graph Neural Architecture Search via Monte-Carlo Tree Search (Full version)](https://mlsanity.com/paper/2308.15734/) (2023)
- [Language Embedding Meets Dynamic Graph: A New Exploration for Neural Architecture Representation Learning](https://mlsanity.com/paper/2506.07735/) (2025)
- [Beyond Model Base Retrieval: Weaving Knowledge to Master Fine-grained Neural Network Design](https://mlsanity.com/paper/2507.15336/) (2025)
- [GraphToxin: Reconstructing Full Unlearned Graphs from Graph Unlearning](https://mlsanity.com/paper/2511.10936/) (2025)
- [Transferable knowledge graphs with executable learned operators for algorithm design](https://mlsanity.com/paper/2603.27922/) (2026)
- [An Efficient and Scalable Graph Condensation with Structure-Preserving](https://mlsanity.com/paper/2605.31016/) (2026)

## Abstract (arXiv)

Graph neural architecture search (GraphNAS) has recently aroused considerable attention in both academia and industry. However, two key challenges seriously hinder the further research of GraphNAS. First, since there is no consensus for the experimental setting, the empirical results in different research papers are often not comparable and even not reproducible, leading to unfair comparisons. Secondly, GraphNAS often needs extensive computations, which makes it highly inefficient and inaccessible to researchers without access to large-scale computation. To solve these challenges, we propose NAS-Bench-Graph, a tailored benchmark that supports unified, reproducible, and efficient evaluations for GraphNAS. Specifically, we construct a unified, expressive yet compact search space, covering 26,206 unique graph neural network (GNN) architectures and propose a principled evaluation protocol. To avoid unnecessary repetitive training, we have trained and evaluated all of these architectures on nine representative graph datasets, recording detailed metrics including train, validation, and test performance in each epoch, the latency, the number of parameters, etc. Based on our proposed benchmark, the performance of GNN architectures can be directly obtained by a look-up table without any further computation, which enables fair, fully reproducible, and efficient comparisons. To demonstrate its usage, we make in-depth analyses of our proposed NAS-Bench-Graph, revealing several interesting findings for GraphNAS. We also showcase how the benchmark can be easily compatible with GraphNAS open libraries such as AutoGL and NNI. To the best of our knowledge, our work is the first benchmark for graph neural architecture search.