| 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 | 2678 | 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 | 463 | 2015 |
| 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 | 226 | 2016 |
| 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 | 207 | 2014 |
| Rotate: Knowledge graph embedding by relational rotation in complex space Z Sun, ZH Deng, JY Nie, J Tang ICLR 2019, 2019 | 202 | 2019 |
| Adversarial network embedding Q Dai, Q Li, J Tang, D Wang arXiv preprint arXiv:1711.07838, 2017 | 113 | 2017 |
| 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 | 94 | 2018 |
| 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 | 81 | 2017 |
| GMNN: Graph Markov Neural Networks M Qu, Y Bengio, J Tang ICML 2019, 2019 | 60 | 2019 |
| Context-aware natural language generation with recurrent neural networks J Tang, Y Yang, S Carton, M Zhang, Q Mei arXiv preprint arXiv:1611.09900, 2016 | 55 | 2016 |
| One theme in all views: modeling consensus topics in multiple contexts J Tang, M Zhang, Q Mei Proceedings of the 19th ACM SIGKDD international conference on Knowledge …, 2013 | 55 | 2013 |
| 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 | 54 | 2019 |
| 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 | 54 | 2019 |
| A trigram hidden Markov model for metadata extraction from heterogeneous references B Ojokoh, M Zhang, J Tang Information Sciences 181 (9), 1538-1551, 2011 | 33 | 2011 |
| 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 | 31 | 2020 |
| Pre-Trained Multi-View Word Embedding Using Two-Side Neural Network. Y Luo, J Tang, J Yan, C Xu, Z Chen AAAI, 1982-1988, 2014 | 31 | 2014 |
| Integrating temporal usage pattern into personalized tag prediction L Zhang, J Tang, M Zhang Asia-Pacific Web Conference, 354-365, 2012 | 30 | 2012 |
| Speaker adaptation of RNN-BLSTM for speech recognition based on speaker code Z Huang, J Tang, S Xue, L Dai 2016 IEEE International Conference on Acoustics, Speech and Signal …, 2016 | 26 | 2016 |
| Voting with their feet: Inferring user preferences from app management activities H Li, W Ai, X Liu, J Tang, G Huang, F Feng, Q Mei Proceedings of the 25th International Conference on World Wide Web, 1351-1362, 2016 | 25 | 2016 |
| Data poisoning attack against unsupervised node embedding methods M Sun, J Tang, H Li, B Li, C Xiao, Y Chen, D Song arXiv preprint arXiv:1810.12881, 2018 | 24 | 2018 |
Ming Zhang, 张铭Professor,Peking UniversityVerified email at net.pku.edu.cn
