| Graph Self-Supervised Learning: A Survey Y Liu, M Jin, S Pan, C Zhou, F Xia, PS Yu IEEE Transactions on Knowledge and Data Engineering, 2022 | 484 | 2022 |
| Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning M Jin, Y Zheng, YF Li, C Gong, C Zhou, S Pan International Joint Conference on Artificial Intelligence (IJCAI), 2021 | 144 | 2021 |
| Time-LLM: Time Series Forecasting by Reprogramming Large Language Models M Jin, S Wang, L Ma, Z Chu, JY Zhang, X Shi, PY Chen, Y Liang, YF Li, ... International Conference on Learning Representations (ICLR), 2024 | 143 | 2024 |
| 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 | 89 | 2021 |
| A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection M Jin, HY Koh, Q Wen, D Zambon, C Alippi, GI Webb, I King, S Pan arXiv preprint arXiv:2307.03759, 2023 | 81 | 2023 |
| 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 | 76 | 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, 2022 | 72 | 2022 |
| Neural Temporal Walks: Motif-Aware Representation Learning on Continuous-Time Dynamic Graphs M Jin, YF Li, S Pan Advances in Neural Information Processing Systems (NeurIPS), 2022 | 60 | 2022 |
| Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects K Zhang, Q Wen, C Zhang, R Cai, M Jin, Y Liu, J Zhang, Y Liang, G Pang, ... IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024 | 57 | 2024 |
| Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook M Jin, Q Wen, Y Liang, C Zhang, S Xue, X Wang, J Zhang, Y Wang, ... arXiv preprint arXiv:2310.10196, 2023 | 55 | 2023 |
| What Can Large Language Models Tell Us about Time Series Analysis M Jin, Y Zhang, W Chen, K Zhang, Y Liang, B Yang, J Wang, S Pan, ... International Conference on Machine Learning (ICML), 2024 | 18* | 2024 |
| Optimized Coefficient Vector and Sparse Representation-Based Classification Method for Face Recognition S Liu, L Li, M Jin, S Hou, Y Peng IEEE Access, 2019 | 17 | 2019 |
| From Unsupervised to Few-shot Graph Anomaly Detection: A Multi-scale Contrastive Learning Approach Y Zheng, M Jin, Y Liu, L Chi, KT Phan, S Pan, YPP Chen arXiv preprint arXiv:2202.05525, 2022 | 14 | 2022 |
| Towards Graph Self-Supervised Learning with Contrastive Adjusted Zooming Y Zheng, M Jin, S Pan, YF Li, H Peng, M Li, Z Li IEEE Transactions on Neural Networks and Learning Systems, 2021 | 14 | 2021 |
| Foundation models for time series analysis: A tutorial and survey Y Liang, H Wen, Y Nie, Y Jiang, M Jin, D Song, S Pan, Q Wen Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and …, 2024 | 13 | 2024 |
| 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, 2023 | 11 | 2023 |
| Geometric Relational Embeddings: A Survey B Xiong, M Nayyeri, M Jin, Y He, M Cochez, S Pan, S Staab arXiv preprint arXiv:2304.11949, 2023 | 8 | 2023 |
| A survey on diffusion models for time series and spatio-temporal data Y Yang, M Jin, H Wen, C Zhang, Y Liang, L Ma, Y Wang, C Liu, B Yang, ... arXiv preprint arXiv:2404.18886, 2024 | 7 | 2024 |
| WeaverBird: Empowering Financial Decision-Making with Large Language Model, Knowledge Base, and Search Engine S Xue, F Zhou, Y Xu, M Jin, Q Wen, H Hao, Q Dai, C Jiang, H Zhao, S Xie, ... arXiv preprint arXiv:2308.05361, 2023 | 7 | 2023 |
| A Clickthrough Rate Prediction Algorithm Based on Users’ Behaviors X Xiong, C Xie, R Zhao, Y Li, S Ju, M Jin IEEE Access, 2019 | 7 | 2019 |
Shirui PanFull Professor, ARC Future Fellow, FQA, Director of TrustAGI Lab, Griffith University在 griffith.edu.au 的电子邮件经过验证
