| Graph self-supervised learning: A survey Y Liu, M Jin, S Pan, C Zhou, Y Zheng, F Xia, SY Philip IEEE transactions on knowledge and data engineering 35 (6), 5879-5900, 2022 | 643 | 2022 |
| Towards Unsupervised Deep Graph Structure Learning Y Liu, Y Zheng, D Zhang, H Chen, H Peng, S Pan The Web Conference (WWW), 2022 | 196 | 2022 |
| Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection Y Zheng, M Jin, Y Liu, L Chi, KT Phan, YPP Chen IEEE Transactions on Knowledge and Data Engineering, 2021 | 148 | 2021 |
| Multivariate time series forecasting with dynamic graph neural odes M Jin, Y Zheng, YF Li, S Chen, B Yang, S Pan IEEE Transactions on Knowledge and Data Engineering 35 (9), 9168-9180, 2022 | 123 | 2022 |
| Anemone: Graph anomaly detection with multi-scale contrastive learning M Jin, Y Liu, Y Zheng, L Chi, YF Li, S Pan Proceedings of the 30th ACM international conference on information …, 2021 | 122 | 2021 |
| A LSTM based model for personalized context-aware citation recommendation L Yang, Y Zheng, X Cai, H Dai, D Mu, L Guo, T Dai IEEE access 6, 59618-59627, 2018 | 101 | 2018 |
| Rethinking and scaling up graph contrastive learning: An extremely efficient approach with group discrimination Y Zheng, S Pan, V Lee, Y Zheng, PS Yu Advances in Neural Information Processing Systems 35, 10809-10820, 2022 | 99 | 2022 |
| Finding the missing-half: Graph complementary learning for homophily-prone and heterophily-prone graphs Y Zheng, H Zhang, V Lee, Y Zheng, X Wang, S Pan International Conference on Machine Learning, 42492-42505, 2023 | 46 | 2023 |
| Correlation-aware spatial–temporal graph learning for multivariate time-series anomaly detection Y Zheng, HY Koh, M Jin, L Chi, KT Phan, S Pan, YPP Chen, W Xiang IEEE Transactions on Neural Networks and Learning Systems 35 (9), 11802-11816, 2023 | 32 | 2023 |
| Clustering social audiences in business information networks Y Zheng, R Hu, S Fung, C Yu, G Long, T Guo, S Pan Pattern Recognition 100, 107126, 2020 | 27 | 2020 |
| From unsupervised to few-shot graph anomaly detection: A multi-scale contrastive learning approach Y Zheng, M Jin, Y Liu, L Chi, KT Phan, YPP Chen arXiv preprint arXiv:2202.05525, 2022 | 20 | 2022 |
| Unifying graph contrastive learning with flexible contextual scopes Y Zheng, Y Zheng, X Zhou, C Gong, VCS Lee, S Pan 2022 IEEE International Conference on Data Mining (ICDM), 793-802, 2022 | 15 | 2022 |
| LGCDA: Predicting CircRNA-disease association based on fusion of local and global features W Lan, C Li, Q Chen, N Yu, Y Pan, Y Zheng, YPP Chen IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2024 | 14 | 2024 |
| ARC: A Generalist Graph Anomaly Detector with In-Context Learning Y Liu, S Li, Y Zheng, Q Chen, C Zhang, S Pan Advances in Neural Information Processing Systems, 2024 | 9 | 2024 |
| Query-oriented citation recommendation based on network correlation L Yang, Y Zheng, X Cai, S Pan, T Dai Journal of Intelligent & Fuzzy Systems 35 (4), 4621-4628, 2018 | 9 | 2018 |
| Ensemble of multiple descriptors for automatic image annotation D He, Y Zheng, S Pan, J Tang 2010 3rd International Congress on Image and Signal Processing 4, 1642-1646, 2010 | 8 | 2010 |
| Graph Spatiotemporal Process for Multivariate Time Series Anomaly Detection with Missing Values Yu Zheng, Huan Yee Koh, Ming Jin, Lianhua Chi, Haishuai Wang, Khoa T. Phan ... Information Fusion 106 (102255), 11, 2024 | 7* | 2024 |
| Graph contrastive learning with kernel dependence maximization for social recommendation X Ni, F Xiong, Y Zheng, L Wang Proceedings of the ACM Web Conference 2024, 481-492, 2024 | 5 | 2024 |
| Selection strategy in graph-based spreading dynamics with limited capacity F Xiong, Y Zheng, W Ding, H Wang, X Wang, H Chen Future Generation Computer Systems 114, 307-317, 2021 | 5 | 2021 |
| Feature-dependent graph convolutional autoencoders with adversarial training methods D Wu, R Hu, Y Zheng, J Jiang, N Sharma, M Blumenstein 2019 International Joint Conference on Neural Networks (IJCNN), 1-8, 2019 | 4 | 2019 |
Ming JinAssistant Professor, School of ICT, Griffith UniversityE-mailová adresa ověřena na: griffith.edu.au