| Bayesian graph convolutional neural networks for semi-supervised classification Y Zhang, S Pal, M Coates, D Üstebay 2019 AAAI Conference on Artificial Intelligence, 2019 | 256 | 2019 |
| Memory Augmented Graph Neural Networks for Sequential Recommendation C Ma, L Ma, Y Zhang, J Sun, X Liu, M Coates 2020 AAAI Conference on Artificial Intelligence, 2020 | 216 | 2020 |
| A graph-cnn for 3d point cloud classification Y Zhang, M Rabbat 2018 IEEE International Conference on Acoustics, Speech and Signal …, 2018 | 180 | 2018 |
| Neighbor interaction aware graph convolution networks for recommendation J Sun, Y Zhang, W Guo, H Guo, R Tang, X He, C Ma, M Coates Proceedings of the 43rd international ACM SIGIR conference on research and …, 2020 | 152 | 2020 |
| Point-of-interest recommendation: Exploiting self-attentive autoencoders with neighbor-aware influence C Ma, Y Zhang, Q Wang, X Liu Proceedings of the 27th ACM international conference on information and …, 2018 | 142 | 2018 |
| Multi-Graph Convolution Collaborative Filtering J Sun, Y Zhang, C Ma, M Coates, H Guo, R Tang, X He 2019 IEEE International Conference on Data Mining (ICDM), 1306-1311, 2019 | 138 | 2019 |
| A framework for recommending accurate and diverse items using bayesian graph convolutional neural networks J Sun, W Guo, D Zhang, Y Zhang, F Regol, Y Hu, H Guo, R Tang, H Yuan, ... Proceedings of the 26th ACM SIGKDD international conference on knowledge …, 2020 | 82 | 2020 |
| Graphsail: Graph structure aware incremental learning for recommender systems Y Xu, Y Zhang, W Guo, H Guo, R Tang, M Coates Proceedings of the 29th ACM International Conference on Information …, 2020 | 80 | 2020 |
| Modeling scale-free graphs with hyperbolic geometry for knowledge-aware recommendation Y Chen, M Yang, Y Zhang, M Zhao, Z Meng, J Hao, I King Proceedings of the fifteenth ACM international conference on web search and …, 2022 | 73 | 2022 |
| Dual graph enhanced embedding neural network for CTR prediction W Guo, R Su, R Tan, H Guo, Y Zhang, Z Liu, R Tang, X He Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data …, 2021 | 56 | 2021 |
| Probabilistic metric learning with adaptive margin for top-k recommendation C Ma, L Ma, Y Zhang, R Tang, X Liu, M Coates Proceedings of the 26th ACM SIGKDD International Conference on knowledge …, 2020 | 52 | 2020 |
| Comparing and Detecting Adversarial Attack for Graph Deep Learning Y Zhang, SH Khan, M Coates 2019 International Conference on Learning Representations, Representation …, 2019 | 36 | 2019 |
| Knowledge-enhanced top-k recommendation in poincaré ball C Ma, L Ma, Y Zhang, H Wu, X Liu, M Coates Proceedings of the AAAI Conference on Artificial Intelligence 35 (5), 4285-4293, 2021 | 34 | 2021 |
| Learning binarized graph representations with multi-faceted quantization reinforcement for top-k recommendation Y Chen, H Guo, Y Zhang, C Ma, R Tang, J Li, I King Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and …, 2022 | 32 | 2022 |
| Bidirectional Learning for Offline Infinite-width Model-based Optimization C Chen, Y Zhang, J Fu, X Liu, M Coates arXiv preprint arXiv:2209.07507, 2022 | 29 | 2022 |
| Structure aware experience replay for incremental learning in graph-based recommender systems K Ahrabian, Y Xu, Y Zhang, J Wu, Y Wang, M Coates Proceedings of the 30th ACM International Conference on Information …, 2021 | 26 | 2021 |
| Optembed: Learning optimal embedding table for click-through rate prediction F Lyu, X Tang, H Zhu, H Guo, Y Zhang, R Tang, X Liu Proceedings of the 31st ACM International Conference on Information …, 2022 | 25 | 2022 |
| Tie: A framework for embedding-based incremental temporal knowledge graph completion J Wu, Y Xu, Y Zhang, C Ma, M Coates, JCK Cheung Proceedings of the 44th international ACM SIGIR conference on research and …, 2021 | 23 | 2021 |
| Generalizable Cross-Graph Embedding for GNN-based Congestion Prediction MC Amur Ghose, Vincent Zhang, Yingxue Zhang, Dong Li, Wulong Liu International Conference On Computer-Aided Design (ICCAD), 2021 | 23* | 2021 |
| RNN with particle flow for probabilistic spatio-temporal forecasting S Pal, L Ma, Y Zhang, M Coates International Conference on Machine Learning, 8336-8348, 2021 | 22 | 2021 |
Mark CoatesProfessor of Electrical Engineering, McGill University在 ece.mcgill.ca 的电子邮件经过验证
