# Rethinking Graph Neural Architecture Search from Message-passing

arXiv: https://arxiv.org/abs/2103.14282 · HTML: https://mlsanity.com/paper/2103.14282/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 46 references that are in the map.
- First posted on arXiv 26 Mar 2021.

## Builds on (most cited first, 29 of 29)

- [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)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [DARTS: Differentiable Architecture Search](https://mlsanity.com/paper/1806.09055/) (2018)
- [Neural Architecture Search with Reinforcement Learning](https://mlsanity.com/paper/1611.01578/) (2016)
- [Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning](https://mlsanity.com/paper/1801.07606/) (2018)
- [Learning Transferable Architectures for Scalable Image Recognition](https://mlsanity.com/paper/1707.07012/) (2017)
- [Regularized Evolution for Image Classifier Architecture Search](https://mlsanity.com/paper/1802.01548/) (2018)
- [DropEdge: Towards Deep Graph Convolutional Networks on Node Classification](https://mlsanity.com/paper/1907.10903/) (2019)
- [Benchmarking Graph Neural Networks](https://mlsanity.com/paper/2003.00982/) (2020)
- [Geometric deep learning on graphs and manifolds using mixture model CNNs](https://mlsanity.com/paper/1611.08402/) (2016)
- [Gradient-based Hyperparameter Optimization through Reversible Learning](https://mlsanity.com/paper/1502.03492/) (2015)
- [Large-Scale Evolution of Image Classifiers](https://mlsanity.com/paper/1703.01041/) (2017)
- [Residual Gated Graph ConvNets](https://mlsanity.com/paper/1711.07553/) (2017)
- [Community Detection and Stochastic Block Models](https://mlsanity.com/paper/1703.10146/) (2017)
- [NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection](https://mlsanity.com/paper/1904.07392/) (2019)
- [Designing Neural Network Architectures using Reinforcement Learning](https://mlsanity.com/paper/1611.02167/) (2016)
- [PC-DARTS: Partial Channel Connections for Memory-Efficient Architecture Search](https://mlsanity.com/paper/1907.05737/) (2019)
- [Hierarchical Representations for Efficient Architecture Search](https://mlsanity.com/paper/1711.00436/) (2017)
- [Understanding and Robustifying Differentiable Architecture Search](https://mlsanity.com/paper/1909.09656/) (2019)
- [DARTS+: Improved Differentiable Architecture Search with Early Stopping](https://mlsanity.com/paper/1909.06035/) (2019)
- [Auto-GNN: Neural Architecture Search of Graph Neural Networks](https://mlsanity.com/paper/1909.03184/) (2019)
- [SGAS: Sequential Greedy Architecture Search](https://mlsanity.com/paper/1912.00195/) (2019)
- [Adaptive Reconstruction Network for Weakly Supervised Referring Expression Grounding](https://mlsanity.com/paper/1908.10568/) (2019)
- [Neural Architecture Search in Graph Neural Networks](https://mlsanity.com/paper/2008.00077/) (2020)
- [PV-NAS: Practical Neural Architecture Search for Video Recognition](https://mlsanity.com/paper/2011.00826/) (2020)
- [Posterior-Guided Neural Architecture Search](https://mlsanity.com/paper/1906.09557/) (2019)

## Built on by (most cited first, 26 of 26)

- [On the Trade-off between Over-smoothing and Over-squashing in Deep Graph Neural Networks](https://mlsanity.com/paper/2212.02374/) (2022)
- [PaSca: a Graph Neural Architecture Search System under the Scalable Paradigm](https://mlsanity.com/paper/2203.00638/) (2022)
- [NAS-Bench-Graph: Benchmarking Graph Neural Architecture Search](https://mlsanity.com/paper/2206.09166/) (2022)
- [GRATIS: Deep Learning Graph Representation with Task-specific Topology and Multi-dimensional Edge Features](https://mlsanity.com/paper/2211.12482/) (2022)
- [Hardware-Aware Graph Neural Network Automated Design for Edge Computing Platforms](https://mlsanity.com/paper/2303.10875/) (2023)
- [Automated Graph Machine Learning: Approaches, Libraries, Benchmarks and Directions](https://mlsanity.com/paper/2201.01288/) (2022)
- [AutoTransfer: AutoML with Knowledge Transfer -- An Application to Graph Neural Networks](https://mlsanity.com/paper/2303.07669/) (2023)
- [Adversarially Robust Neural Architecture Search for Graph Neural Networks](https://mlsanity.com/paper/2304.04168/) (2023)
- [Automatic Relation-aware Graph Network Proliferation](https://mlsanity.com/paper/2205.15678/) (2022)
- [Unsupervised Graph Neural Architecture Search with Disentangled Self-supervision](https://mlsanity.com/paper/2403.05064/) (2024)
- [Explore Contextual Information for 3D Scene Graph Generation](https://mlsanity.com/paper/2210.06240/) (2022)
- [Generalizing Aggregation Functions in GNNs:High-Capacity GNNs via Nonlinear Neighborhood Aggregators](https://mlsanity.com/paper/2202.09145/) (2022)
- [Edge-featured Graph Neural Architecture Search](https://mlsanity.com/paper/2109.01356/) (2021)
- [Towards Lightweight Graph Neural Network Search with Curriculum Graph Sparsification](https://mlsanity.com/paper/2406.16357/) (2024)
- [DFG-NAS: Deep and Flexible Graph Neural Architecture Search](https://mlsanity.com/paper/2206.08582/) (2022)
- [Graph Neural Architecture Search with GPT-4](https://mlsanity.com/paper/2310.01436/) (2023)
- [A Graph Neural Architecture Search Approach for Identifying Bots in Social Media](https://mlsanity.com/paper/2411.16285/) (2024)
- [Behavior Importance-Aware Graph Neural Architecture Search for Cross-Domain Recommendation](https://mlsanity.com/paper/2504.07102/) (2025)
- [Efficient Automatic Machine Learning via Design Graphs](https://mlsanity.com/paper/2210.12257/) (2022)
- [Causal-aware Graph Neural Architecture Search under Distribution Shifts](https://mlsanity.com/paper/2405.16489/) (2024)
- [Graph in Graph Neural Network](https://mlsanity.com/paper/2407.00696/) (2024)
- [HGNAS: Hardware-Aware Graph Neural Architecture Search for Edge Devices](https://mlsanity.com/paper/2408.12840/) (2024)
- [Spatio-Temporal Fuzzy-oriented Multi-Modal Meta-Learning for Fine-grained Emotion Recognition](https://mlsanity.com/paper/2412.13541/) (2024)
- [How Wide and How Deep? Mitigating Over-Squashing of GNNs via Channel Capacity Constrained Estimation](https://mlsanity.com/paper/2511.06443/) (2025)
- [GCoDE: Efficient Device-Edge Co-Inference for GNNs via Architecture-Mapping Co-Search](https://mlsanity.com/paper/2512.11856/) (2025)
- [PIMPC-GNN: Physics-Informed Multi-Phase Consensus Learning for Enhancing Imbalanced Node Classification in Graph Neural Networks](https://mlsanity.com/paper/2602.01920/) (2026)

## Abstract (arXiv)

Graph neural networks (GNNs) emerged recently as a standard toolkit for learning from data on graphs. Current GNN designing works depend on immense human expertise to explore different message-passing mechanisms, and require manual enumeration to determine the proper message-passing depth. Inspired by the strong searching capability of neural architecture search (NAS) in CNN, this paper proposes Graph Neural Architecture Search (GNAS) with novel-designed search space. The GNAS can automatically learn better architecture with the optimal depth of message passing on the graph. Specifically, we design Graph Neural Architecture Paradigm (GAP) with tree-topology computation procedure and two types of fine-grained atomic operations (feature filtering and neighbor aggregation) from message-passing mechanism to construct powerful graph network search space. Feature filtering performs adaptive feature selection, and neighbor aggregation captures structural information and calculates neighbors' statistics. Experiments show that our GNAS can search for better GNNs with multiple message-passing mechanisms and optimal message-passing depth. The searched network achieves remarkable improvement over state-of-the-art manual designed and search-based GNNs on five large-scale datasets at three classical graph tasks. Codes can be found at https://github.com/phython96/GNAS-MP.