| 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 | 5064 | 2015 |
| 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 | 803 | 2015 |
| Cotype: Joint extraction of typed entities and relations with knowledge bases X Ren, Z Wu, W He, M Qu, CR Voss, H Ji, TF Abdelzaher, J Han Proceedings of the 26th international conference on world wide web, 1015-1024, 2017 | 260 | 2017 |
| Gmnn: Graph markov neural networks M Qu, Y Bengio, J Tang International conference on machine learning, 5241-5250, 2019 | 244 | 2019 |
| Meta-path guided embedding for similarity search in large-scale heterogeneous information networks J Shang, M Qu, J Liu, LM Kaplan, J Han, J Peng arXiv preprint arXiv:1610.09769, 2016 | 160 | 2016 |
| 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 | 149 | 2017 |
| Afet: Automatic fine-grained entity typing by hierarchical partial-label embedding X Ren, W He, M Qu, L Huang, H Ji, J Han Proceedings of the 2016 conference on empirical methods in natural language …, 2016 | 140 | 2016 |
| Label noise reduction in entity typing by heterogeneous partial-label embedding X Ren, W He, M Qu, CR Voss, H Ji, J Han Proceedings of the 22nd ACM SIGKDD international conference on Knowledge …, 2016 | 135 | 2016 |
| Recurrent event network: Autoregressive structure inference over temporal knowledge graphs W Jin, M Qu, X Jin, X Ren arXiv preprint arXiv:1904.05530, 2019 | 133 | 2019 |
| Probabilistic logic neural networks for reasoning M Qu, J Tang Advances in neural information processing systems 32, 2019 | 121 | 2019 |
| Graphvite: A high-performance cpu-gpu hybrid system for node embedding Z Zhu, S Xu, J Tang, M Qu The World Wide Web Conference, 2494-2504, 2019 | 95 | 2019 |
| Graphmix: Improved training of gnns for semi-supervised learning V Verma, M Qu, K Kawaguchi, A Lamb, Y Bengio, J Kannala, J Tang Proceedings of the AAAI conference on artificial intelligence 35 (11), 10024 …, 2021 | 92 | 2021 |
| vgraph: A generative model for joint community detection and node representation learning FY Sun, M Qu, J Hoffmann, CW Huang, J Tang Advances in Neural Information Processing Systems 32, 2019 | 69 | 2019 |
| Few-shot relation extraction via bayesian meta-learning on relation graphs M Qu, T Gao, LP Xhonneux, J Tang International conference on machine learning, 7867-7876, 2020 | 65 | 2020 |
| Continuous graph neural networks LP Xhonneux, M Qu, J Tang International Conference on Machine Learning, 10432-10441, 2020 | 61 | 2020 |
| Rnnlogic: Learning logic rules for reasoning on knowledge graphs M Qu, J Chen, LP Xhonneux, Y Bengio, J Tang arXiv preprint arXiv:2010.04029, 2020 | 56 | 2020 |
| Automatic synonym discovery with knowledge bases M Qu, X Ren, J Han Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge …, 2017 | 47 | 2017 |
| Covi white paper H Alsdurf, E Belliveau, Y Bengio, T Deleu, P Gupta, D Ippolito, R Janda, ... arXiv preprint arXiv:2005.08502, 2020 | 41 | 2020 |
| Curriculum learning for heterogeneous star network embedding via deep reinforcement learning M Qu, J Tang, J Han Proceedings of the Eleventh ACM International Conference on Web Search and …, 2018 | 41 | 2018 |
| Weakly-supervised relation extraction by pattern-enhanced embedding learning M Qu, X Ren, Y Zhang, J Han Proceedings of the 2018 World Wide Web Conference, 1257-1266, 2018 | 40 | 2018 |
