Kun Kuang
Impact in
- Artificial Intelligence top 1%
- Topic Modeling
- Advanced Graph Neural Networks
- Domain Adaptation and Few-Shot Learning
- Privacy-Preserving Technologies in Data
- Natural Language Processing Techniques
- Health Informatics top 10%
Papers in
-
- Domain Adaptation and Few-Shot Learning 20
- Topic Modeling 17
- Advanced Graph Neural Networks 11
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- Multimodal Machine Learning Applications 10
- Co-authors
- Fei Wu (55 shared papers)Peng Cui (12 shared papers)Jianxin Ma (3 shared papers)Jiwei Li (2 shared papers)Xiaofei Sun (2 shared papers)Yuxian Meng (2 shared papers)Qinghong Han (1 shared paper)Xin Wang (3 shared papers)
- Journals
- IEEE Transactions on Knowledge and Data Engineering (9 papers)Engineering (6 papers)ACM Transactions on Knowledge Discovery from Data (3 papers)ACM Transactions on Information Systems (2 papers)Artificial Intelligence and Law (2 papers)
- Partner nations
- ChinaUnited StatesSingapore
In The Last Decade
Kun Kuang
90 papers receiving 1.2k citations
Peers
Comparison fields: 5 of 111
- Artificial Intelligence 838
- Health Informatics 19
- Computer Vision and Pattern Recognition 248
- Information Systems 198
- Statistics and Probability 67
Countries citing papers authored by Kun Kuang
This map shows the geographic impact of Kun Kuang'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 Kun Kuang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kun Kuang more than expected).
Fields of papers citing papers by Kun Kuang
This network shows the impact of papers produced by Kun Kuang. 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 Kun Kuang. The network helps show where Kun Kuang may publish in the future.
Co-authors
The 25 scholars most cited alongside Kun Kuang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 102 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2021 | 118 | |
| 2 | 2020 | 91 | |
| 3 | 2022 | 79 | |
| 4 | Disentangled Graph Convolutional Networks | 2019 | 75 |
| 5 | 2020 | 63 | |
| 6 | 2020 | 54 | |
| 7 | 2022 | 51 | |
| 8 | 2023 | 43 | |
| 9 | 2021 | 32 | |
| 10 | 2023 | 30 | |
| 11 | 2022 | 28 | |
| 12 | 2023 | 23 | |
| 13 | 2022 | 23 | |
| 14 | 2021 | 21 | |
| 15 | 2022 | 19 | |
| 16 | 2022 | 19 | |
| 17 | 2022 | 19 | |
| 18 | 2024 | 18 | |
| 19 | 2023 | 17 | |
| 20 | 2022 | 17 |
About Kun Kuang
Kun Kuang is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems, Statistics and Probability and Political Science and International Relations, having authored 102 papers that have together received 1.2k indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (20 papers), Topic Modeling (17 papers), Artificial Intelligence in Law (12 papers), Statistical Methods and Inference (11 papers), Recommender Systems and Techniques (11 papers), Advanced Graph Neural Networks (11 papers), Advanced Causal Inference Techniques (11 papers) and Multimodal Machine Learning Applications (10 papers). The work is most often cited by research in Artificial Intelligence (838 citations), Health Informatics (19 citations), Computer Vision and Pattern Recognition (248 citations), Information Systems (198 citations) and Statistics and Probability (67 citations). Kun Kuang has collaborated with scholars based in China, United States and Singapore. Frequent co-authors include Fei Wu, Peng Cui, Jianxin Ma, Jiwei Li, Xiaofei Sun, Yuxian Meng, Qinghong Han, Xin Wang, Changlong Sun and Ruoxuan Xiong. Their work appears in journals such as IEEE Transactions on Knowledge and Data Engineering, Engineering, ACM Transactions on Knowledge Discovery from Data, ACM Transactions on Information Systems and Artificial Intelligence and Law.
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.