| 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 | 6439 | 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 | 965 | 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 | 348 | 2017 |
| Recurrent event network: Autoregressive structure inference over temporal knowledge graphs W Jin, M Qu, X Jin, X Ren arXiv preprint arXiv:1904.05530, 2019 | 335 | 2019 |
| Gmnn: Graph markov neural networks M Qu, Y Bengio, J Tang International conference on machine learning, 5241-5250, 2019 | 330 | 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 | 205 | 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 | 196 | 2017 |
| 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 | 191 | 2020 |
| Probabilistic logic neural networks for reasoning M Qu, J Tang Advances in neural information processing systems 32, 2019 | 189 | 2019 |
| 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 | 172 | 2016 |
| 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 | 171 | 2021 |
| 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 | 170 | 2016 |
| Continuous graph neural networks LP Xhonneux, M Qu, J Tang International conference on machine learning, 10432-10441, 2020 | 159 | 2020 |
| 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 | 147 | 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 | 132 | 2020 |
| 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 | 108 | 2019 |
| Recurrent event network: Global structure inference over temporal knowledge graph W Jin, H Jiang, M Qu, T Chen, C Zhang, P Szekely, X Ren | 86 | 2019 |
| Learning dual retrieval module for semi-supervised relation extraction H Lin, J Yan, M Qu, X Ren The world wide web conference, 1073-1083, 2019 | 69 | 2019 |
| Collaborative policy learning for open knowledge graph reasoning C Fu, T Chen, M Qu, W Jin, X Ren arXiv preprint arXiv:1909.00230, 2019 | 68 | 2019 |
| 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 | 67 | 2018 |
