This repository lists recent automated graph neural network papers. Welcome to recommend missing references!

1. GraphNAS: Graph Neural Architecture Search with Reinforcement Learning. Yang Gao, Hong Yang, Peng Zhang, Chuan Zhou, Yue Hu. CoRR 2019. link
2. Graph Neural Architecture Search. Yang Gao, Hong Yang, Peng Zhang, Chuan Zhou, Yue Hu. In IJCAI'20. link
3. Auto-GNN: Neural Architecture Search of Graph Neural Networks. Kaixiong Zhou, Qingquan Song, Xiao Huang, Xia Hu. CoRR 2019. link
4. Evolutionary Architecture Search for Graph Neural Networks. Min Shi, David A.Wilson, Xingquan Zhu, Yu Huang, Yuan Zhuang, Jianxun Liu, Yufei Tang. CoRR 2020. link
5. Probabilistic Dual Network Architecture Search on Graphs, Yiren Zhao, Duo Wang, Xitong Gao, Robert D. Mullins, Pietro Liò, Mateja Jamnik. CoRR 2020. link
6. AutoGraph: Automated Graph Neural Network. Yaoman Li, Irwin King. In ICONIP'20. link
7. Graph Neural Network Architecture Search for Molecular Property Prediction. In BigData'20. link
8. DiffMG: Differentiable Meta Graph Search for Heterogeneous Graph Neural Networks. Yuhui Ding, Quanming Yao, Huan Zhao, Tong Zhang. CoRR 2020.
9. Design Space for Graph Neural Networks. Jiaxuan You, Zhitao Ying, Jure Leskovec. In NeurIPS'20. link
10. Rethinking Graph Neural Network Search from Message-passing. Shaofei Cai, Liang Li, Jincan Deng, Beichen Zhang, Zheng-Jun Zha, Li Su, Qingming Huang. CoRR 2021. link
11. One-shot Graph Neural Architecture Search with Dynamic Search Space. Yanxi Li, Zean Wen, Yunhe Wang, Chang Xu. In AAAI'21. link
12. AutoGEL: An Automated Graph Neural Network with Explicit Link Information. Zhili Wang, Shimin DI, Lei Chen. In NeurIPS'21.

1. Policy-GNN: Aggregation Optimization for Graph Neural Networks. Kwei-Herng Lai, Daochen Zha, Kaixiong Zhou, Xia Hu. In KDD'20. link
2. Simplifying Architecture Search for Graph Neural Network. Huan Zhao, Lanning Wei, Quanming Yao. In CIKM Workshop'20. link
3. Search to aggregate neighborhood for graph neural network. Huan Zhao, Quanming Yao, Weiwei Tu. In ICDE'21. link
4. AutoAttend: Automated Attention Representation Search. Chaoyu Guan, Xin Wang, Wenwu Zhu. In ICML'21. link

1. Learning Graph Convolutional Network for Skeleton-Based Human Action Recognition by Neural Searching. Wei Peng, Xiaopeng Hong, Haoyu Chen, Guoying Zhao. In AAAI'20. link

1. Using Graph Convolution Network for Predicting Performance of Automatically Generated Convolution Neural Networks. Enzhi Zhang, Tomohiro Harada, R. Thawonmas. In CSDE'19. link
2. A Variational-Sequential Graph Autoencoder for Neural ArchitecturePerformance Prediction. David Friede, Jovita Lukasik, H. Stuckenschmidt, Margret Keuper. CoRR 2019. link
3. Neural Architecture Performance Prediction Using Graph Neural Networks. Jovita Lukasik, David Friede, H. Stuckenschmidt, Margret Keuper. link