Min Yang
Impact in
- Artificial Intelligence top 0.2%
- Topic Modeling
- Sentiment Analysis and Opinion Mining
- Advanced Text Analysis Techniques
- Text and Document Classification Technologies
- Natural Language Processing Techniques
- Advanced Graph Neural Networks
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- Multimodal Machine Learning Applications
Papers in
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- Topic Modeling 77
- Natural Language Processing Techniques 30
- Advanced Text Analysis Techniques 30
- Text and Document Classification Technologies 29
- Sentiment Analysis and Opinion Mining 29
- Advanced Graph Neural Networks 24
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- Recommender Systems and Techniques 17
Min Yang
173 papers receiving 3.5k citations
Peers
Comparison fields: 5 of 148
- Artificial Intelligence 2.6k
- Computer Vision and Pattern Recognition 784
- Information Systems 557
- Statistical and Nonlinear Physics 216
- Health Informatics 20
Countries citing papers authored by Min Yang
This map shows the geographic impact of Min Yang'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 Min Yang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Min Yang more than expected).
Fields of papers citing papers by Min Yang
This network shows the impact of papers produced by Min Yang. 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 Min Yang. The network helps show where Min Yang may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Min Yang, 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 | 0 | |
| 2 | 2025 | 0 | |
| 3 | 2024 | 1 | |
| 4 | 2024 | 1 | |
| 5 | 2024 | 8 | |
| 6 | 2024 | 3 | |
| 7 | 2023 | 4 | |
| 8 | 2023 | 2 | |
| 9 | 2023 | 2 | |
| 10 | 2022 | 7 | |
| 11 | 2022 | 75 | |
| 12 | 2020 | 5 | |
| 13 | 2019 | 15 | |
| 14 | 2019 | 18 | |
| 15 | 2019 | 19 | |
| 16 | 2019 | 36 | |
| 17 | 2019 | 24 | |
| 18 | 2018 | 4 | |
| 19 | 2018 | 26 | |
| 20 | 2015 | 6 |
About Min Yang
Min Yang is a scholar working on Artificial Intelligence, Information Systems, Computer Vision and Pattern Recognition, Management Science and Operations Research and Statistical and Nonlinear Physics, having authored 186 papers that have together received 3.7k indexed citations. Recurring topics across this work include Topic Modeling (77 papers), Natural Language Processing Techniques (30 papers), Advanced Text Analysis Techniques (30 papers), Text and Document Classification Technologies (29 papers), Sentiment Analysis and Opinion Mining (29 papers), Advanced Graph Neural Networks (24 papers), Recommender Systems and Techniques (17 papers) and Multimodal Machine Learning Applications (16 papers). The work is most often cited by research in Artificial Intelligence (2.6k citations), Computer Vision and Pattern Recognition (784 citations), Information Systems (557 citations), Statistical and Nonlinear Physics (216 citations) and Health Informatics (20 citations). Min Yang has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Ying Shen, Xiaojun Chen, Zhou Zhao, Ruifeng Xu, Kai Lei, Qiang Qu, Wei Zhao, Wenting Tu, Zeyang Lei and Jia Zhu. Their work appears in journals such as Information Sciences, IEEE Access, Knowledge-Based Systems, Neural Computing and Applications and IEEE Transactions on Cybernetics.
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.