Yu Zheng

Orcid: 0000-0003-0757-4210

Affiliations:

Monash University, Faculty of Information Technology, Clayton, Australia
La Trobe University, Melbourne, Australia
Northwest A&F University, College of Information Engineering, YangLing, China (former)

According to our database¹, Yu Zheng authored at least 20 papers between 2018 and 2023.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of four.

Timeline

Legend:

Book

In proceedings

Article

PhD thesis

Dataset

Other

Bibliography

2023

Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection.

[BibT_eX]

[DOI]

IEEE Trans. Knowl. Data Eng., December, 2023

Multivariate Time Series Forecasting With Dynamic Graph Neural ODEs.

[BibT_eX]

[DOI]

IEEE Trans. Knowl. Data Eng., September, 2023

Graph Self-Supervised Learning: A Survey.

[BibT_eX]

[DOI]

IEEE Trans. Knowl. Data Eng., June, 2023

Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook.

[BibT_eX]

[DOI]

CoRR, 2023

Correlation-aware Spatial-Temporal Graph Learning for Multivariate Time-series Anomaly Detection.

[BibT_eX]

[DOI]

CoRR, 2023

Finding the Missing-half: Graph Complementary Learning for Homophily-prone and Heterophily-prone Graphs.

[BibT_eX]

[DOI]

Yizhen Zheng

He Zhang

Vincent Cheng-Siong Lee

Yu Zheng

Xiao Wang

Shirui Pan

Proceedings of the International Conference on Machine Learning, 2023

2022

From Unsupervised to Few-shot Graph Anomaly Detection: A Multi-scale Contrastive Learning Approach.

[BibT_eX]

[DOI]

CoRR, 2022

Towards Unsupervised Deep Graph Structure Learning.

[BibT_eX]

[DOI]

Proceedings of the WWW '22: The ACM Web Conference 2022, Virtual Event, Lyon, France, April 25, 2022

Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Unifying Graph Contrastive Learning with Flexible Contextual Scopes.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Data Mining, 2022

2021

Selection strategy in graph-based spreading dynamics with limited capacity.

[BibT_eX]

[DOI]

Future Gener. Comput. Syst., 2021

ANEMONE: Graph Anomaly Detection with Multi-Scale Contrastive Learning.

[BibT_eX]

[DOI]

Proceedings of the CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1, 2021

2020

Clustering social audiences in business information networks.

[BibT_eX]

[DOI]

Pattern Recognit., 2020