| FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling J Chen, T Ma, C Xiao International Conference on Learning Representations (ICLR), 2018 | 1438 | 2018 |
| EvolveGCN: Evolving graph convolutional networks for dynamic graphs A Pareja, G Domeniconi, J Chen, T Ma, T Suzumura, H Kanezashi, ... Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 5363-5370, 2020 | 744 | 2020 |
| DAG-GNN: DAG structure learning with graph neural networks Y Yu, J Chen, T Gao, M Yu International Conference on Machine Learning, 7154-7163, 2019 | 319 | 2019 |
| Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics M Weber, G Domeniconi, J Chen, DKI Weidele, C Bellei, T Robinson, ... arXiv preprint arXiv:1908.02591, 2019 | 317 | 2019 |
| Fast approximate kNN graph construction for high dimensional data via recursive Lanczos bisection J Chen, H Fang, Y Saad Journal of Machine Learning Research 10 (Sep), 1989-2012, 2009 | 311 | 2009 |
| Dense subgraph extraction with application to community detection J Chen, Y Saad IEEE Transactions on Knowledge and Data Engineering 24 (7), 1216-1230, 2012 | 279 | 2012 |
| Trace optimization and eigenproblems in dimension reduction methods E Kokiopoulou, J Chen, Y Saad Numerical Linear Algebra with Applications 18 (3), 565-602, 2011 | 218 | 2011 |
| Constrained generation of semantically valid graphs via regularizing variational autoencoders T Ma, J Chen, C Xiao Advances in Neural Information Processing Systems 31, 7113-7124, 2018 | 214 | 2018 |
| Fast Estimation of via Stochastic Lanczos Quadrature S Ubaru, J Chen, Y Saad SIAM Journal on Matrix Analysis and Applications 38 (4), 1075-1099, 2017 | 166 | 2017 |
| Architectural modeling from sparsely scanned range data J Chen, B Chen International Journal of Computer Vision 78 (2), 223-236, 2008 | 144 | 2008 |
| Project CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks R Puri, DS Kung, G Janssen, W Zhang, G Domeniconi, V Zolotov, J Dolby, ... arXiv preprint arXiv:2105.12655, 2021 | 143 | 2021 |
| Discrete Graph Structure Learning for Forecasting Multiple Time Series C Shang, J Chen, J Bi International Conference on Learning Representations (ICLR), 2021 | 133 | 2021 |
| On the tensor SVD and the optimal low rank orthogonal approximation of tensors J Chen, Y Saad SIAM Journal on Matrix Analysis and Applications 30 (4), 1709-1734, 2009 | 123 | 2009 |
| Stochastic approximation of score functions for Gaussian processes M Stein, J Chen, M Anitescu Annals of Applied Statistics 7 (2), 1162--1191, 2013 | 93 | 2013 |
| Algebraic distance on graphs J Chen, I Safro SIAM Journal on Scientific Computing 33 (6), 3468-3490, 2011 | 90 | 2011 |
| Scalable Graph Learning for Anti-Money Laundering: A First Look M Weber, J Chen, T Suzumura, A Pareja, T Ma, H Kanezashi, T Kaler, ... arXiv preprint arXiv:1812.00076, 2018 | 89 | 2018 |
| A matrix-free approach for solving the parametric Gaussian process maximum likelihood problem M Anitescu, J Chen, L Wang SIAM Journal on Scientific Computing 34 (1), A240-A262, 2012 | 87 | 2012 |
| Directed Acyclic Graph Neural Networks V Thost, J Chen International Conference on Learning Representations (ICLR), 2021 | 65 | 2021 |
| Lanczos vectors versus singular vectors for effective dimension reduction J Chen, Y Saad IEEE Transactions on Knowledge and Data Engineering 21 (8), 1091-1103, 2009 | 59 | 2009 |
| Computing f(A)b via Least Squares Polynomial Approximations J Chen, M Anitescu, Y Saad SIAM Journal on Scientific Computing 33 (1), 195-222, 2011 | 56 | 2011 |
Tengfei MaStony Brook UniversityVerified email at stonybrook.edu
