Jun Zhou
- Artificial Intelligence top 1%
- Advanced Graph Neural Networks 34
- Topic Modeling 27
- Machine Learning and Data Classification 14
- Data Stream Mining Techniques 12
- Imbalanced Data Classification Techniques 11
- Domain Adaptation and Few-Shot Learning 11
- Information Systems top 1%
- Recommender Systems and Techniques 44
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- Advanced Bandit Algorithms Research 10
- Co-authors
- Xiaolong LiChaochao ChenQi YuanLongfei LiXinxing YangZiqi LiuLe SongZhiqiang Zhang
- Journals
- Proceedings of the VLDB Endowment (4 papers)IEEE Transactions on Knowledge and Data Engineering (4 papers)ACM Transactions on Information Systems (2 papers)
- Partner nations
- ChinaUnited StatesAustralia
In The Last Decade
Jun Zhou
133 papers receiving 1.8k citations
Peers
Comparison fields: 5 of 132
- Artificial Intelligence 1.3k
- Information Systems 595
- Computer Vision and Pattern Recognition 345
- Statistical and Nonlinear Physics 148
- Computer Networks and Communications 248
Countries citing papers authored by Jun Zhou
This map shows the geographic impact of Jun Zhou's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Jun Zhou with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jun Zhou more than expected).
Fields of papers citing papers by Jun Zhou
This network shows the impact of papers produced by Jun Zhou. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Jun Zhou. The network helps show where Jun Zhou may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Jun Zhou, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 7 | |
| 2 | 2025 | 1 | |
| 3 | 2025 | 0 | |
| 4 | 2024 | 12 | |
| 5 | 2024 | 3 | |
| 6 | 2024 | 0 | |
| 7 | 2024 | 2 | |
| 8 | 2024 | 0 | |
| 9 | 2023 | 5 | |
| 10 | 2023 | 3 | |
| 11 | 2023 | 3 | |
| 12 | 2023 | 4 | |
| 13 | 2023 | 1 | |
| 14 | 2023 | 0 | |
| 15 | 2023 | 1 | |
| 16 | 2023 | 1 | |
| 17 | 2023 | 5 | |
| 18 | 2022 | 11 | |
| 19 | 2021 | 46 | |
| 20 | 2018 | 4 |
About Jun Zhou
Jun Zhou is a scholar working on Artificial Intelligence, Information Systems, Computer Vision and Pattern Recognition, Space and Planetary Science and Management Science and Operations Research, having authored 161 papers that have together received 1.9k indexed citations. Recurring topics across this work include Recommender Systems and Techniques (44 papers), Advanced Graph Neural Networks (34 papers), Topic Modeling (27 papers), Machine Learning and Data Classification (14 papers), Data Stream Mining Techniques (12 papers), Imbalanced Data Classification Techniques (11 papers), Domain Adaptation and Few-Shot Learning (11 papers) and Advanced Bandit Algorithms Research (10 papers). The work is most often cited by research in Artificial Intelligence (1.3k citations), Information Systems (595 citations), Computer Vision and Pattern Recognition (345 citations), Statistical and Nonlinear Physics (148 citations) and Computer Networks and Communications (248 citations). Jun Zhou has collaborated with scholars based in China, United States and Australia. Frequent co-authors include Xiaolong Li, Chaochao Chen, Qi Yuan, Longfei Li, Xinxing Yang, Ziqi Liu, Le Song, Zhiqiang Zhang, Ziqi Liu and Le Song. Their work appears in journals such as Proceedings of the VLDB Endowment, IEEE Transactions on Knowledge and Data Engineering, ACM Transactions on Information Systems, ACM Transactions on Knowledge Discovery from Data and IEEE Transactions on Dependable and Secure Computing.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.