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Ming Jin
Ming Jin
Ph.D. Candidate, Monash University
在 monash.edu 的电子邮件经过验证 - 首页
标题
引用次数
引用次数
年份
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
1542022
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, 2021
562021
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
262021
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
192021
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
132019
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
82022
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
72021
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, 2022
52022
Searching Correlated Patterns From Graph Streams
M Jin, M Li, Y Zheng, L Chi
IEEE Access, 2020
52020
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
42022
A Clickthrough Rate Prediction Algorithm Based on Users’ Behaviors
X Xiong, C Xie, R Zhao, Y Li, S Ju, M Jin
IEEE Access, 2019
42019
Spatiotemporal Representation Learning on Time Series with Dynamic Graph ODEs
M Jin, YF Li, Y Zheng, B Yang, S Pan
12022
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