| Line: Large-scale information network embedding J Tang, M Qu, M Wang, M Zhang, J Yan, Q Mei Proceedings of the 24th international conference on world wide web, 1067-1077, 2015 | 5189 | 2015 |
| Rotate: Knowledge graph embedding by relational rotation in complex space Z Sun, ZH Deng, JY Nie, J Tang ICLR 2019, 2019 | 1341 | 2019 |
| Pte: Predictive text embedding through large-scale heterogeneous text networks J Tang, M Qu, Q Mei Proceedings of the 21th ACM SIGKDD international conference on knowledge …, 2015 | 819 | 2015 |
| InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization FY Sun, J Hoffmann, V Verma, J Tang ICLR 2020 (Spotlight), 2020 | 498 | 2020 |
| Visualizing large-scale and high-dimensional data J Tang, J Liu, M Zhang, Q Mei Proceedings of the 25th international conference on world wide web, 287-297, 2016 | 426 | 2016 |
| AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks W Song, C Shi, Z Xiao, Z Duan, Y Xu, M Zhang, J Tang CIKM 2019, 2019 | 411 | 2019 |
| Deepinf: Social influence prediction with deep learning J Qiu, J Tang, H Ma, Y Dong, K Wang, J Tang Proceedings of the 24th ACM SIGKDD international conference on knowledge …, 2018 | 398 | 2018 |
| Artificial intelligence in COVID-19 drug repurposing Y Zhou, F Wang, J Tang, R Nussinov, F Cheng The Lancet Digital Health 2 (12), e667-e676, 2020 | 347 | 2020 |
| Understanding the limiting factors of topic modeling via posterior contraction analysis J Tang, Z Meng, X Nguyen, Q Mei, M Zhang International conference on machine learning, 190-198, 2014 | 329 | 2014 |
| Session-based social recommendation via dynamic graph attention networks W Song, Z Xiao, Y Wang, L Charlin, M Zhang, J Tang Proceedings of the Twelfth ACM international conference on web search and …, 2019 | 305 | 2019 |
| KEPLER: A unified model for knowledge embedding and pre-trained language representation X Wang, T Gao, Z Zhu, Z Zhang, Z Liu, J Li, J Tang Transactions of the Association for Computational Linguistics 9, 176-194, 2021 | 300 | 2021 |
| GMNN: Graph Markov Neural Networks M Qu, Y Bengio, J Tang ICML 2019, 2019 | 254 | 2019 |
| Adversarial network embedding Q Dai, Q Li, J Tang, D Wang Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 230 | 2018 |
| GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation C Shi, M Xu, Z Zhu, W Zhang, M Zhang, J Tang ICLR 2020, 2020 | 220 | 2020 |
| An attention-based collaboration framework for multi-view network representation learning M Qu, J Tang, J Shang, X Ren, M Zhang, J Han Proceedings of the 2017 ACM on Conference on Information and Knowledge …, 2017 | 156 | 2017 |
| Probabilistic Logic Neural Networks for Reasoning M Qu, J Tang NeurIPS'19, 2019 | 129 | 2019 |
| Utilizing graph machine learning within drug discovery and development T Gaudelet, B Day, AR Jamasb, J Soman, C Regep, G Liu, JBR Hayter, ... Briefings in bioinformatics 22 (6), bbab159, 2021 | 120 | 2021 |
| Graphmix: Regularized training of graph neural networks for semi-supervised learning V Verma, M Qu, A Lamb, Y Bengio, J Kannala, J Tang AAAI'21, 2021 | 99* | 2021 |
| GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation M Xu, L Yu, Y Song, C Shi, S Ermon, J Tang ICLR 2022 Oral, 2022 | 98 | 2022 |
| GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding Z Zhu, S Xu, M Qu, J Tang The World Wide Web Conference, 2494-2504, 2019 | 98 | 2019 |
Meng QuQuebec AI Institute (Mila)umontreal.ca의 이메일 확인됨
