| Inductive representation learning on large graphs WL Hamilton, R Ying, J Leskovec arXiv preprint arXiv:1706.02216, 2017 | 2851 | 2017 |
| Representation learning on graphs: Methods and applications WL Hamilton, R Ying, J Leskovec arXiv preprint arXiv:1709.05584, 2017 | 931 | 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 | 768 | 2018 |
| Hierarchical graph representation learning with differentiable pooling R Ying, J You, C Morris, X Ren, WL Hamilton, J Leskovec arXiv preprint arXiv:1806.08804, 2018 | 344 | 2018 |
| Graphrnn: Generating realistic graphs with deep auto-regressive models J You, R Ying, X Ren, W Hamilton, J Leskovec International Conference on Machine Learning, 5708-5717, 2018 | 332 | 2018 |
| Graph convolutional policy network for goal-directed molecular graph generation J You, B Liu, R Ying, V Pande, J Leskovec arXiv preprint arXiv:1806.02473, 2018 | 270 | 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 | 225 | 2018 |
| Position-aware graph neural networks J You, R Ying, J Leskovec International Conference on Machine Learning, 7134-7143, 2019 | 93 | 2019 |
| Gnnexplainer: Generating explanations for graph neural networks R Ying, D Bourgeois, J You, M Zitnik, J Leskovec Advances in neural information processing systems 32, 9240, 2019 | 80 | 2019 |
| Hyperbolic graph convolutional neural networks I Chami, R Ying, C Ré, J Leskovec Advances in neural information processing systems 32, 4869, 2019 | 72 | 2019 |
| Learning to simulate complex physics with graph networks A Sanchez-Gonzalez, J Godwin, T Pfaff, R Ying, J Leskovec, P Battaglia International Conference on Machine Learning, 8459-8468, 2020 | 57 | 2020 |
| Neural execution of graph algorithms P Veličković, R Ying, M Padovano, R Hadsell, C Blundell arXiv preprint arXiv:1910.10593, 2019 | 28 | 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 | 28 | 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 | 23 | 2015 |
| Improving graph attention networks with large margin-based constraints G Wang, R Ying, J Huang, J Leskovec arXiv preprint arXiv:1910.11945, 2019 | 14 | 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 | 13 | 2016 |
| 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 |
| Design space for graph neural networks J You, R Ying, J Leskovec arXiv preprint arXiv:2011.08843, 2020 | 2 | 2020 |
| Direct Multi-hop Attention based Graph Neural Network G Wang, R Ying, J Huang, J Leskovec arXiv preprint arXiv:2009.14332, 2020 | 2 | 2020 |
| Redundancy-Free Computation for Graph Neural Networks Z Jia, S Lin, R Ying, J You, J Leskovec, A Aiken Proceedings of the 26th ACM SIGKDD International Conference on Knowledge …, 2020 | 1 | 2020 |
Jure LeskovecProfessor of Computer Science, Stanford UniversityVerified email at cs.stanford.edu
