| Graph Random Neural Network for Semi-Supervised Learning on Graphs W Feng, J Zhang, Y Dong, Y Han, H Luan, Q Xu, Q Yang, E Kharlamov, ... Advances in Neural Information Processing Systems 33, 2020 | 426 | 2020 |
| Are we really making much progress? revisiting, benchmarking and refining heterogeneous graph neural networks Q Lv, M Ding, Q Liu, Y Chen, W Feng, S He, C Zhou, J Jiang, Y Dong, ... Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data …, 2021 | 302 | 2021 |
| Understanding dropouts in MOOCs W Feng, J Tang, TX Liu Proceedings of the AAAI conference on artificial intelligence 33 (01), 517-524, 2019 | 280 | 2019 |
| Mixgcf: An improved training method for graph neural network-based recommender systems T Huang, Y Dong, M Ding, Z Yang, W Feng, X Wang, J Tang Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data …, 2021 | 198 | 2021 |
| Attentional graph convolutional networks for knowledge concept recommendation in moocs in a heterogeneous view J Gong, S Wang, J Wang, W Feng, H Peng, J Tang, PS Yu Proceedings of the 43rd international ACM SIGIR conference on research and …, 2020 | 180 | 2020 |
| MOOCCube: A large-scale data repository for NLP applications in MOOCs J Yu, G Luo, T Xiao, Q Zhong, Y Wang, W Feng, J Luo, C Wang, L Hou, ... Proceedings of the 58th annual meeting of the association for computational …, 2020 | 137 | 2020 |
| GRAND+: Scalable Graph Random Neural Networks W Feng, Y Dong, T Huang, Z Yin, X Cheng, E Kharlamov, J Tang Proceedings of the ACM Web Conference 2022, 3248-3258, 2022 | 35 | 2022 |
| MOOCCubeX: a large knowledge-centered repository for adaptive learning in MOOCs J Yu, Y Wang, Q Zhong, G Luo, Y Mao, K Sun, W Feng, W Xu, S Cao, ... Proceedings of the 30th ACM International Conference on Information …, 2021 | 35 | 2021 |
| Reinforced moocs concept recommendation in heterogeneous information networks J Gong, Y Wan, Y Liu, X Li, Y Zhao, C Wang, Y Lin, X Fang, W Feng, ... ACM Transactions on the Web 17 (3), 1-27, 2023 | 29 | 2023 |
| Wingnn: Dynamic graph neural networks with random gradient aggregation window Y Zhu, F Cong, D Zhang, W Gong, Q Lin, W Feng, Y Dong, J Tang Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and …, 2023 | 27 | 2023 |
| ApeGNN: node-wise adaptive aggregation in GNNs for recommendation D Zhang, Y Zhu, Y Dong, Y Wang, W Feng, E Kharlamov, J Tang Proceedings of the ACM Web Conference 2023, 759-769, 2023 | 22 | 2023 |
| Beihang-msra at semeval-2017 task 3: A ranking system with neural matching features for community question answering W Feng, Y Wu, W Wu, Z Li, M Zhou Proceedings of the 11th International Workshop on Semantic Evaluation …, 2017 | 19 | 2017 |
| Dropconn: dropout connection based random gnns for molecular property prediction D Zhang, W Feng, Y Wang, Z Qi, Y Shan, J Tang IEEE Transactions on Knowledge and Data Engineering 36 (2), 518-529, 2023 | 10 | 2023 |
| Course concept extraction in MOOC via explicit/implicit representation X Wang, W Feng, J Tang, Q Zhong 2018 IEEE Third International Conference on Data Science in Cyberspace (DSC …, 2018 | 8 | 2018 |
| Semi-Supervised Social Bot Detection with Initial Residual Relation Attention Networks M Zhou, W Feng, Y Zhu, D Zhang, Y Dong, J Tang PKDD 2023 Best Student Paper; Joint European Conference on Machine Learning …, 2023 | 7 | 2023 |
| SCR: Training Graph Neural Networks with Consistency Regularization C Zhang, Y He, Y Cen, Z Hou, W Feng, Y Dong, X Cheng, H Cai, F He, ... arXiv preprint arXiv:2112.04319, 2021 | 5 | 2021 |
| XiaoMu: an AI-driven assistant for MOOCs Z Song, J Tang, TX Liu, W Zheng, L Wu, W Feng, J Zhang Science China. Information Sciences 64 (6), 164101, 2021 | 3 | 2021 |
Tang JieWeBank Chair Professor, Tsinghua University在 tsinghua.edu.cn 的电子邮件经过验证
