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Recent years have witnessed an upsurge in research interests and applications of machine learning on graphs.
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A. A. Hagberg, D. A. Schult, and P. J. Swart, “Exploring network structure, dynamics, and function using networkx,” in Proceedings of the 7th Python in Science Conference , 2008, pp. 11 – 15
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2013
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P. Yanardag and S. Vishwanathan, “Deep graph kernels,” in Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining , 2015, pp. 1365–1374
2015
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B. Zoph and Q. Le, “Neural architecture search with reinforcement learning,” in International Conference on Learning Representations , 2016
2016
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M. Defferrard, X. Bresson, and P. Vandergheynst, “Convolutional neural networks on graphs with fast localized spectral filtering,” Advances in neural information processing systems , vol. 29, 2016
2016
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X. Wang, P. Cui, J. Wang, J. Pei, W. Zhu, and S. Yang, “Community preserving network embedding,” in Proceedings of the AAAI conference on artificial intelligence , vol. 31, no. 1, 2017
2017
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M. M. Bronstein, J. Bruna, Y. LeCun, A. Szlam, and P. Vandergheynst, “Geometric deep learning: Going beyond euclidean data,” IEEE Signal Process. Mag. , vol. 34, no. 4, pp. 18–42, 2017
2017
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W. L. Hamilton, R. Ying, and J. Leskovec, “Inductive representation learning on large graphs,” in Proceedings of the 31st International Conference on Neural Information Processing Systems , 2017, pp. 1025–1035
2017
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G. Ke, Q. Meng, T. Finley, T. Wang, W. Chen, W. Ma, Q. Ye, and T.-Y. Liu, “Lightgbm: A highly efficient gradient boosting decision tree,” in Advances in Neural Information Processing Systems , vol. 30, 2017
2017
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D. Golovin, B. Solnik, S. Moitra, G. Kochanski, J. Karro, and D. Sculley, “Google vizier: A service for black-box optimization,” in Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining , 2017, pp. 1487–1495
2017
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T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” in International Conference on Learning Representations (ICLR) , 2017
2017
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2017
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2018
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2018
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O. Shchur, M. Mumme, A. Bojchevski, and S. Günnemann, “Pitfalls of graph neural network evaluation,” Relational Representation Learning Workshop, NeurIPS 2018 , 2018
2018
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R. A. Rossi, R. Zhou, and N. K. Ahmed, “Deep inductive graph representation learning,” IEEE Transactions on Knowledge and Data Engineering , vol. 32, no. 3, pp. 438–452, 2018
2018
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A. Tsitsulin, D. Mottin, P. Karras, A. Bronstein, and E. Müller, “Netlsd: hearing the shape of a graph,” in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , 2018, pp. 2347–2356
2018
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H. Pham, M. Guan, B. Zoph, Q. Le, and J. Dean, “Efficient neural architecture search via parameters sharing,” in International conference on machine learning , 2018, pp. 4095–4104
2018
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P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio, “Graph Attention Networks,” International Conference on Learning Representations , 2018
2018
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M. Zhang, Z. Cui, M. Neumann, and Y. Chen, “An end-to-end deep learning architecture for graph classification,” in Proceedings of the AAAI conference on artificial intelligence , vol. 32, no. 1, 2018
2018
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K. Xu, C. Li, Y. Tian, T. Sonobe, K.-i. Kawarabayashi, and S. Jegelka, “Representation learning on graphs with jumping knowledge networks,” in International conference on machine learning , 2018, pp. 5453–5462
2018
Cited alongside, same era.
J. Ma, C. Zhou, P. Cui, H. Yang, and W. Zhu, “Learning disentangled representations for recommendation,” in Advances in Neural Information Processing Systems , vol. 32, 2019
2019
Cited alongside, same era.
K. Tu, J. Ma, P. Cui, J. Pei, and W. Zhu, “Autone: Hyperparameter optimization for massive network embedding,” in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , 2019, pp. 216–225
2019
Cited alongside, same era.
M. Fey and J. E. Lenssen, “Fast graph representation learning with PyTorch Geometric,” in ICLR Workshop on Representation Learning on Graphs and Manifolds , 2019
2019
Cited alongside, same era.
H. Li, X. Wang, Z. Zhang, J. Ma, P. Cui, and W. Zhu, “Intention-aware sequential recommendation with structured intent transition,” IEEE Transactions on Knowledge and Data Engineering , 2021
2021
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Z. Zhang, X. Wang, and W. Zhu, “Automated machine learning on graphs: A survey,” in Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence , 2021, survey track
2021
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Z. Zhang, P. Cui, J. Pei, X. Wang, and W. Zhu, “Eigen-gnn: A graph structure preserving plug-in for gnns,” IEEE Transactions on Knowledge and Data Engineering , 2021
2021
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C. Guan, X. Wang, and W. Zhu, “Autoattend: Automated attention representation search,” in International conference on machine learning , 2021, pp. 3864–3874
2021
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2019
Cited alongside, same era.
R. Zhu, K. Zhao, H. Yang, W. Lin, C. Zhou, B. Ai, Y. Li, and J. Zhou, “Aligraph: A comprehensive graph neural network platform,” Proc. VLDB Endow. , vol. 12, no. 12, p. 2094–2105, 2019
2019
Cited alongside, same era.
A. Lerer, L. Wu, J. Shen, T. Lacroix, L. Wehrstedt, A. Bose, and A. Peysakhovich, “PyTorch-BigGraph: A Large-scale Graph Embedding System,” in Proceedings of the 2nd SysML Conference , 2019
2019
Cited alongside, same era.
M. Feurer, A. Klein, K. Eggensperger, J. T. Springenberg, M. Blum, and F. Hutter, “Auto-sklearn: efficient and robust automated machine learning,” in Automated Machine Learning . Springer, Cham, 2019, pp. 113–134
2019
Cited alongside, same era.
H. Wu, C. Wang, Y. Tyshetskiy, A. Docherty, K. Lu, and L. Zhu, “Adversarial examples for graph data: deep insights into attack and defense,” in Proceedings of the 28th International Joint Conference on Artificial Intelligence , 2019, pp. 4816–4823
2019
Cited alongside, same era.
T. Elsken, J. H. Metzen, and F. Hutter, “Neural architecture search: A survey,” The Journal of Machine Learning Research , vol. 20, no. 1, pp. 1997–2017, 2019
2019
Cited alongside, same era.
H. Liu, K. Simonyan, and Y. Yang, “Darts: Differentiable architecture search,” in International Conference on Learning Representations , 2019
2019
Cited alongside, same era.
J. Wu, X.-Y. Chen, H. Zhang, L.-D. Xiong, H. Lei, and S.-H. Deng, “Hyperparameter optimization for machine learning models based on bayesian optimization,” Journal of Electronic Science and Technology , vol. 17, no. 1, pp. 26–40, 2019
2019
Cited alongside, same era.
2021
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H. Benmeziane, K. El Maghraoui, H. Ouarnoughi, S. Niar, M. Wistuba, and N. Wang, “Hardware-aware neural architecture search: Survey and taxonomy,” in Thirtieth International Joint Conference on Artificial Intelligence { \{ IJCAI-21 } \} , 2021, pp. 4322–4329
2021
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Y. Qin, X. Wang, Z. Zhang, and W. Zhu, “Graph differentiable architecture search with structure learning,” Advances in neural information processing systems , vol. 34, pp. 16 860–16 872, 2021
2021
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Y. Li, W. Jin, H. Xu, and J. Tang, “Deeprobust: a platform for adversarial attacks and defenses,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 35, no. 18, 2021, pp. 16 078–16 080
2021
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M. Jin, H. Chang, W. Zhu, and S. Sojoudi, “Power up! robust graph convolutional network via graph powering,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 35, no. 9, 2021, pp. 8004–8012
2021
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J. Wang, A. Ma, Y. Chang, J. Gong, Y. Jiang, R. Qi, C. Wang, H. Fu, Q. Ma, and D. Xu, “scgnn is a novel graph neural network framework for single-cell rna-seq analyses,” Nature communications , vol. 12, no. 1, p. 1882, 2021
2021
Closest in time.
F. M. Bianchi, D. Grattarola, L. Livi, and C. Alippi, “Graph neural networks with convolutional arma filters,” IEEE transactions on pattern analysis and machine intelligence , vol. 44, no. 7, pp. 3496–3507, 2021
2021
Closest in time.
G. Li, M. Muller, A. Thabet, and B. Ghanem, “Deepgcns: Can gcns go as deep as cnns?” in Proceedings of the IEEE/CVF international conference on computer vision , 2019, pp. 9267–9276
2021
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W. Jiang and J. Luo, “Graph neural network for traffic forecasting: A survey,” Expert Systems with Applications , vol. 207, p. 117921, 2022
2022
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Y. Gao, P. Zhang, H. Yang, C. Zhou, Z. Tian, Y. Hu, Z. Li, and J. Zhou, “Graphnas++: Distributed architecture search for graph neural networks,” IEEE Transactions on Knowledge and Data Engineering , 2022
2022
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K. Zhou, X. Huang, Q. Song, R. Chen, and X. Hu, “Auto-gnn: Neural architecture search of graph neural networks,” Frontiers in big Data , vol. 5, p. 1029307, 2022
2022
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Y. Liu, M. Jin, S. Pan, C. Zhou, Y. Zheng, F. Xia, and S. Y. Philip, “Graph self-supervised learning: A survey,” IEEE Transactions on Knowledge and Data Engineering , vol. 35, no. 6, pp. 5879–5900, 2022
2022
Closest in time.
W. Jin, X. Liu, X. Zhao, Y. Ma, N. Shah, and J. Tang, “Automated self-supervised learning for graphs,” in The 10th International Conference on Learning Representations (ICLR 2022) , 2022
2022
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L. Sun, Y. Dou, C. Yang, K. Zhang, J. Wang, S. Y. Philip, L. He, and B. Li, “Adversarial attack and defense on graph data: A survey,” IEEE Transactions on Knowledge and Data Engineering , 2022
2022
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Y. Qin, X. Wang, Z. Zhang, P. Xie, and W. Zhu, “Graph neural architecture search under distribution shifts,” in International Conference on Machine Learning , 2022, pp. 18 083–18 095
2022
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C. Guan, X. Wang, H. Chen, Z. Zhang, and W. Zhu, “Large-scale graph neural architecture search,” in International Conference on Machine Learning , 2022, pp. 7968–7981
2022
Closest in time.
Y. Wang, J. Wang, Z. Cao, and A. Barati Farimani, “Molecular contrastive learning of representations via graph neural networks,” Nature Machine Intelligence , vol. 4, no. 3, pp. 279–287, 2022
2022
Closest in time.
Y. Qin, Z. Zhang, X. Wang, Z. Zhang, and W. Zhu, “Nas-bench-graph: Benchmarking graph neural architecture search,” in Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track , 2022
2022
Closest in time.
Y. Gao, P. Zhang, C. Zhou, H. Yang, Z. Li, Y. Hu, and S. Y. Philip, “Hgnas++: efficient architecture search for heterogeneous graph neural networks,” IEEE Transactions on Knowledge and Data Engineering , 2023
2023
Closest in time.
C. Wang, B. Chen, G. Li, and H. Wang, “Automated graph neural network search under federated learning framework,” IEEE Transactions on Knowledge and Data Engineering , 2023
2023
Closest in time.
B. Xie, H. Chang, Z. Zhang, X. Wang, D. Wang, Z. Zhang, Z. Ying, and W. Zhu, “Adversarially robust neural architecture search for graph neural networks,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023
2023
Closest in time.
Z. Zhang, X. Wang, C. Guan, Z. Zhang, H. Li, and W. Zhu, “Autogt: Automated graph transformer architecture search,” in The Eleventh International Conference on Learning Representations , 2023
2023
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2023
Closest in time.
Z. Zhang, X. Wang, Z. Zhang, G. Shen, S. Shen, and W. Zhu, “Unsupervised graph neural architecture search with disentangled self-supervision,” Advances in Neural Information Processing Systems , vol. 36, 2023
2023
Closest in time.
Y. Qin, X. Wang, Z. Zhang, H. Chen, and W. Zhu, “Multi-task graph neural architecture search with task-aware collaboration and curriculum,” Advances in neural information processing systems , vol. 36, 2023
2023
Closest in time.
B. M. Oloulade, J. Gao, J. Chen, R. Al-Sabri, and Z. Wu, “Cancer drug response prediction with surrogate modeling-based graph neural architecture search,” Bioinformatics , vol. 39, no. 8, 2023
2023
Closest in time.
G. Jin, H. Yan, F. Li, Y. Li, and J. Huang, “Dual graph convolution architecture search for travel time estimation,” ACM Transactions on Intelligent Systems and Technology , vol. 14, no. 4, pp. 1–23, 2023
2023
Closest in time.
X. Wang, H. Ji, C. Shi, B. Wang, Y. Ye, P. Cui, and P. S. Yu, “Heterogeneous graph attention network,” in The world wide web conference , 2019, pp. 2022–2032
2032
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