Inductive representation learning on large graphs W Hamilton, Z Ying, J Leskovec Advances in neural information processing systems, 1024-1034, 2017 | 1806 | 2017 |

Representation learning on graphs: Methods and applications WL Hamilton, R Ying, J Leskovec arXiv preprint arXiv:1709.05584, 2017 | 674 | 2017 |

Graph convolutional neural networks for web-scale recommender systems R Ying, R He, K Chen, P Eksombatchai, WL Hamilton, J Leskovec Proceedings of the 24th ACM SIGKDD International Conference on Knowledge …, 2018 | 458 | 2018 |

Hierarchical graph representation learning with differentiable pooling Z Ying, J You, C Morris, X Ren, W Hamilton, J Leskovec Advances in neural information processing systems, 4800-4810, 2018 | 201 | 2018 |

Graph convolutional policy network for goal-directed molecular graph generation J You, B Liu, Z Ying, V Pande, J Leskovec Advances in neural information processing systems, 6410-6421, 2018 | 182 | 2018 |

Graphrnn: Generating realistic graphs with deep auto-regressive models J You, R Ying, X Ren, WL Hamilton, J Leskovec arXiv preprint arXiv:1802.08773, 2018 | 180 | 2018 |

Jiaxuan You, Christopher Morris, Xiang Ren, Will Hamilton, and Jure Leskovec. Hierarchical graph representation learning with differentiable pooling Z Ying Advances in neural information processing systems 31, 4800-4810, 2018 | 173 | 2018 |

Graphrnn: A deep generative model for graphs J You, R Ying, X Ren, WL Hamilton, J Leskovec arXiv preprint arXiv:1802.08773, 2018 | 66 | 2018 |

Position-aware graph neural networks J You, R Ying, J Leskovec arXiv preprint arXiv:1906.04817, 2019 | 44 | 2019 |

Gnnexplainer: Generating explanations for graph neural networks Z Ying, D Bourgeois, J You, M Zitnik, J Leskovec Advances in neural information processing systems, 9244-9255, 2019 | 32 | 2019 |

Hyperbolic graph convolutional neural networks I Chami, Z Ying, C Ré, J Leskovec Advances in neural information processing systems, 4868-4879, 2019 | 27 | 2019 |

Gnn explainer: A tool for post-hoc explanation of graph neural networks R Ying, D Bourgeois, J You, M Zitnik, J Leskovec arXiv preprint arXiv:1903.03894, 2019 | 25 | 2019 |

Approximating dynamic time warping and edit distance for a pair of point sequences PK Agarwal, K Fox, J Pan, R Ying arXiv preprint arXiv:1512.01876, 2015 | 20 | 2015 |

Learning to simulate complex physics with graph networks A Sanchez-Gonzalez, J Godwin, T Pfaff, R Ying, J Leskovec, PW Battaglia arXiv preprint arXiv:2002.09405, 2020 | 14 | 2020 |

Neural execution of graph algorithms P Veličković, R Ying, M Padovano, R Hadsell, C Blundell arXiv preprint arXiv:1910.10593, 2019 | 12 | 2019 |

Representation learning on graphs: methods and applications (2017) WL Hamilton, R Ying, J Leskovec arXiv preprint arXiv:1709.05584, 2019 | 11 | 2019 |

A simple efficient approximation algorithm for dynamic time warping R Ying, J Pan, K Fox, PK Agarwal Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances …, 2016 | 11 | 2016 |

Improving graph attention networks with large margin-based constraints G Wang, R Ying, J Huang, J Leskovec arXiv preprint arXiv:1910.11945, 2019 | 4 | 2019 |

A method for discrimination of noise and EMG signal regions recorded during rhythmic behaviors R Ying, CE Wall Journal of Biomechanics 49 (16), 4113-4118, 2016 | 3 | 2016 |

Redundancy-free computation graphs for graph neural networks Z Jia, S Lin, R Ying, J You, J Leskovec, A Aiken arXiv preprint arXiv:1906.03707, 2019 | 1 | 2019 |