# Neural Architecture Search in Graph Neural Networks

arXiv: https://arxiv.org/abs/2008.00077 · HTML: https://mlsanity.com/paper/2008.00077/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2020.

## Built on by (most cited first, 15 of 15)

- [Neural Architecture Search: Insights from 1000 Papers](https://mlsanity.com/paper/2301.08727/) (2023)
- [Automated Machine Learning on Graphs: A Survey](https://mlsanity.com/paper/2103.00742/) (2021)
- [Rethinking Graph Neural Architecture Search from Message-passing](https://mlsanity.com/paper/2103.14282/) (2021)
- [NAS-Bench-Graph: Benchmarking Graph Neural Architecture Search](https://mlsanity.com/paper/2206.09166/) (2022)
- [Automated Graph Machine Learning: Approaches, Libraries, Benchmarks and Directions](https://mlsanity.com/paper/2201.01288/) (2022)
- [A Novel Genetic Algorithm with Hierarchical Evaluation Strategy for Hyperparameter Optimisation of Graph Neural Networks](https://mlsanity.com/paper/2101.09300/) (2021)
- [Unsupervised Graph Neural Architecture Search with Disentangled Self-supervision](https://mlsanity.com/paper/2403.05064/) (2024)
- [Edge-featured Graph Neural Architecture Search](https://mlsanity.com/paper/2109.01356/) (2021)
- [AutoHEnsGNN: Winning Solution to AutoGraph Challenge for KDD Cup 2020](https://mlsanity.com/paper/2111.12952/) (2021)
- [Meta-multigraph Search: Rethinking Meta-structure on Heterogeneous Information Networks](https://mlsanity.com/paper/2304.11574/) (2023)
- [ProGNNosis: A Data-driven Model to Predict GNN Computation Time Using Graph Metrics](https://mlsanity.com/paper/2206.08258/) (2022)
- [Automated Polynomial Filter Learning for Graph Neural Networks](https://mlsanity.com/paper/2307.07956/) (2023)
- [NodeNAS: Node-Specific Graph Neural Architecture Search for Out-of-Distribution Generalization](https://mlsanity.com/paper/2503.02448/) (2025)
- [Can Graph Foundation Models Generalize Over Architecture?](https://mlsanity.com/paper/2603.22984/) (2026)
- [Eleven Years of BRACIS: A Meta-Scientific Study of the Brazilian Conference on Intelligent Systems](https://mlsanity.com/paper/2608.09964/) (2026)

## Abstract (arXiv)

Performing analytical tasks over graph data has become increasingly interesting due to the ubiquity and large availability of relational information. However, unlike images or sentences, there is no notion of sequence in networks. Nodes (and edges) follow no absolute order, and it is hard for traditional machine learning (ML) algorithms to recognize a pattern and generalize their predictions on this type of data. Graph Neural Networks (GNN) successfully tackled this problem. They became popular after the generalization of the convolution concept to the graph domain. However, they possess a large number of hyperparameters and their design and optimization is currently hand-made, based on heuristics or empirical intuition. Neural Architecture Search (NAS) methods appear as an interesting solution to this problem. In this direction, this paper compares two NAS methods for optimizing GNN: one based on reinforcement learning and a second based on evolutionary algorithms. Results consider 7 datasets over two search spaces and show that both methods obtain similar accuracies to a random search, raising the question of how many of the search space dimensions are actually relevant to the problem.