| Discrete-time temporal network embedding via implicit hierarchical learning in hyperbolic space M Yang, M Zhou, M Kalander, Z Huang, I King Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data …, 2021 | 130 | 2021 |
| Scaling up graph neural networks via graph coarsening Z Huang, S Zhang, C Xi, T Liu, M Zhou Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data …, 2021 | 121 | 2021 |
| HRCF: Enhancing collaborative filtering via hyperbolic geometric regularization M Yang, M Zhou, J Liu, D Lian, I King Proceedings of the ACM web conference 2022, 2462-2471, 2022 | 88 | 2022 |
| Hyperbolic graph neural networks: A review of methods and applications M Yang, M Zhou, Z Li, J Liu, L Pan, H Xiong, I King arXiv preprint arXiv:2202.13852, 2022 | 78 | 2022 |
| HICF: Hyperbolic Informative Collaborative Filtering M Yang, Z Li, M Zhou, J Liu, I King Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and …, 2022 | 71 | 2022 |
| A survey of pattern mining in dynamic graphs P Fournier‐Viger, G He, C Cheng, J Li, M Zhou, JCW Lin, U Yun Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 10 (6 …, 2020 | 64 | 2020 |
| Hyperbolic Representation Learning: Revisiting and Advancing M Yang, M Zhou, R Ying, Y Chen, I King ICML 2023, 2023 | 32 | 2023 |
| Hyperbolic temporal network embedding M Yang, M Zhou, H Xiong, I King IEEE Transactions on Knowledge and Data Engineering 35 (11), 11489-11502, 2022 | 32 | 2022 |
| Enhancing hyperbolic graph embeddings via contrastive learning J Liu, M Yang, M Zhou, S Feng, P Fournier-Viger NeurIPS 2021 Workshop: Self-Supervised Learning - Theory and Practice, 2022 | 27 | 2022 |
| κHGCN: Tree-likeness Modeling via Continuous and Discrete Curvature Learning M Yang, M Zhou, L Pan, I King Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and …, 2023 | 23 | 2023 |
| TeleGraph: A Benchmark Dataset for Hierarchical Link Prediction M Zhou, B Li, M Yang, L Pan Workshop on Graph Learning Benchmarks@TheWebConf 2022, 2022 | 23 | 2022 |
| Spatio-Temporal Hybrid Graph Convolutional Network for Traffic Forecasting in Telecommunication Networks M Kalander, M Zhou, C Zhang, H Yi, L Pan https://arxiv.org/abs/2009.09849, 2020 | 23 | 2020 |
| An influence-based approach for root cause alarm discovery in telecom networks K Zhang, M Kalander, M Zhou, X Zhang, J Ye International Conference on Service-Oriented Computing, 124-136, 2020 | 21 | 2020 |
| Discovering alarm correlation rules for network fault management P Fournier-Viger, G He, M Zhou, M Nouioua, J Liu International Conference on Service-Oriented Computing, 228-239, 2020 | 18 | 2020 |
| A survey of machine learning for network fault management M Nouioua, P Fournier-Viger, G He, F Nouioua, Z Min Machine Learning and Data Mining for Emerging Trend in Cyber Dynamics …, 2021 | 17 | 2021 |
| Online model regression for nonlinear time-varying manufacturing systems J Hu, M Zhou, X Li, Z Xu Automatica 78, 163-173, 2017 | 16 | 2017 |
| Hyperbolic graph neural networks: A tutorial on methods and applications M Zhou, M Yang, B Xiong, H Xiong, I King Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and …, 2023 | 14 | 2023 |
| MM-FRec: Multi-modal enhanced fashion item recommendation X Song, C Wang, C Sun, S Feng, M Zhou, L Nie IEEE Transactions on Knowledge and Data Engineering 35 (10), 10072-10084, 2023 | 13 | 2023 |
| Traffic4cast at neurips 2022–predict dynamics along graph edges from sparse node data: Whole city traffic and eta from stationary vehicle detectors M Neun, C Eichenberger, H Martin, M Spanring, R Siripurapu, D Springer, ... NeurIPS 2022 Competition Track, 251-278, 2023 | 12 | 2023 |
| Mata*: combining learnable node matching with a* algorithm for approximate graph edit distance computation J Liu, M Zhou, S Ma, L Pan Proceedings of the 32nd ACM International Conference on Information and …, 2023 | 11 | 2023 |
Menglin YangAssistant Professor, HKUST(GZ)Correu electrònic verificat a hkust-gz.edu.cn