Ming He
Impact in
- Information Systems top 5%
- Recommender Systems and Techniques
- Artificial Intelligence top 10%
- Advanced Graph Neural Networks
- Topic Modeling
- Sentiment Analysis and Opinion Mining
Papers in ⓘ
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- Advanced Graph Neural Networks 10
- Topic Modeling 10
- Sentiment Analysis and Opinion Mining 4
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- Recommender Systems and Techniques 13
- Co-authors
- Pinhua Rao (1 shared paper)Hongke Zhao (9 shared papers)Jianping Fan (6 shared papers)Chuang Zhao (6 shared papers)Dingyong Wang (2 shared papers)Shouqin Sun (2 shared papers)Jian Zhang (1 shared paper)Enhong Chen (4 shared papers)
- Journals
- IEEE Transactions on Knowledge and Data Engineering (3 papers)IEEE Access (2 papers)ACM Transactions on the Web (2 papers)Neurocomputing (2 papers)Environmental Monitoring and Assessment (2 papers)
- Partner nations
- ChinaHong KongUnited States
In The Last Decade
Ming He
44 papers receiving 462 citations
Peers
Comparison fields: 5 of 104
- Information Systems 126
- Artificial Intelligence 157
- Pollution 49
- Computer Vision and Pattern Recognition 73
- Health, Toxicology and Mutagenesis 39
Countries citing papers authored by Ming He
This map shows the geographic impact of Ming He'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 Ming He with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ming He more than expected).
Fields of papers citing papers by Ming He
This network shows the impact of papers produced by Ming He. 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 Ming He. The network helps show where Ming He may publish in the future.
Co-authors
The 25 scholars most cited alongside Ming He, 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 48 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2005 | 66 | |
| 2 | 2023 | 57 | |
| 3 | 2008 | 33 | |
| 4 | 2023 | 29 | |
| 5 | 2023 | 29 | |
| 6 | 2017 | 26 | |
| 7 | 2015 | 24 | |
| 8 | 2023 | 23 | |
| 9 | 2022 | 20 | |
| 10 | 2003 | 18 | |
| 11 | 2007 | 12 | |
| 12 | 2022 | 12 | |
| 13 | 2023 | 12 | |
| 14 | 2022 | 11 | |
| 15 | 2021 | 10 | |
| 16 | 2023 | 8 | |
| 17 | 2025 | 7 | |
| 18 | 2022 | 7 | |
| 19 | 2019 | 6 | |
| 20 | 2022 | 6 |
About Ming He
Ming He is a scholar working on Artificial Intelligence, Information Systems, Family Practice, Computer Vision and Pattern Recognition and Statistical and Nonlinear Physics, having authored 48 papers that have together received 475 indexed citations. Recurring topics across this work include Recommender Systems and Techniques (13 papers), Advanced Graph Neural Networks (10 papers), Topic Modeling (10 papers), Sentiment Analysis and Opinion Mining (4 papers), Advanced Image and Video Retrieval Techniques (3 papers), COVID-19 diagnosis using AI (2 papers), Consumer Market Behavior and Pricing (2 papers) and Lichen and fungal ecology (2 papers). The work is most often cited by research in Information Systems (126 citations), Artificial Intelligence (157 citations), Pollution (49 citations), Computer Vision and Pattern Recognition (73 citations) and Health, Toxicology and Mutagenesis (39 citations). Ming He has collaborated with scholars based in China, Hong Kong and United States. Frequent co-authors include Pinhua Rao, Hongke Zhao, Jianping Fan, Chuang Zhao, Dingyong Wang, Shouqin Sun, Jian Zhang, Enhong Chen, Yanru Zeng and Xuesong Wang. Their work appears in journals such as IEEE Transactions on Knowledge and Data Engineering, IEEE Access, ACM Transactions on the Web, Neurocomputing and Environmental Monitoring and Assessment.
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.