Guangyong Chen
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- Computational Drug Discovery Methods 7
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- Advanced Neural Network Applications 4
- Video Surveillance and Tracking Methods 3
- Geology top 10%
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- Machine Learning in Materials Science 9
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- Protein Structure and Dynamics 5
- RNA and protein synthesis mechanisms 3
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- Topic Modeling 4
- Domain Adaptation and Few-Shot Learning 4
- Journals
- Journal of the American Chemical Society (1 paper)Nature Communications (3 papers)SHILAP Revista de lepidopterología (1 paper)
- Partner nations
- ChinaHong KongUnited States
In The Last Decade
Guangyong Chen
57 papers receiving 1.2k citations
Hit Papers
Peers
Comparison fields: 5 of 143
- Computational Theory and Mathematics 197
- Computer Vision and Pattern Recognition 229
- Cancer Research 130
- Computer Graphics and Computer-Aided Design 26
- Geology 36
Countries citing papers authored by Guangyong Chen
This map shows the geographic impact of Guangyong Chen'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 Guangyong Chen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Guangyong Chen more than expected).
Fields of papers citing papers by Guangyong Chen
This network shows the impact of papers produced by Guangyong Chen. 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 Guangyong Chen. The network helps show where Guangyong Chen may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Guangyong Chen, 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 | 2024 | 2 | |
| 3 | 2024 | 2 | |
| 4 | 2024 | 24 | |
| 5 | A Survey on Generative Diffusion Modelsbreakdown → | 2024 | 192 |
| 6 | 2024 | 0 | |
| 7 | 2024 | 0 | |
| 8 | 2024 | 2 | |
| 9 | 2023 | 11 | |
| 10 | 2023 | 9 | |
| 11 | 2023 | 80 | |
| 12 | 2023 | 1 | |
| 13 | 2022 | 1 | |
| 14 | 2021 | 60 | |
| 15 | 2021 | 2 | |
| 16 | 2019 | 4 | |
| 17 | 2017 | 108 | |
| 18 | Learning to Aggregate Ordinal Labels by Maximizing Separating Width | 2017 | 4 |
| 19 | 2016 | 12 | |
| 20 | Research Progress in Bioactivity and Synthesis of β-caryophyllene and Its Derivatives | 2012 | 0 |
About Guangyong Chen
Guangyong Chen is a scholar working on Acoustics and Ultrasonics, Computer Vision and Pattern Recognition and Computational Theory and Mathematics, having authored 67 papers that have together received 1.2k indexed citations. Recurring topics across this work include Machine Learning in Materials Science (9 papers), Computational Drug Discovery Methods (7 papers), Protein Structure and Dynamics (5 papers), Topic Modeling (4 papers), Domain Adaptation and Few-Shot Learning (4 papers), Advanced Neural Network Applications (4 papers), Video Surveillance and Tracking Methods (3 papers) and RNA and protein synthesis mechanisms (3 papers). The work is most often cited by research in Computational Theory and Mathematics (197 citations), Computer Vision and Pattern Recognition (229 citations) and Cancer Research (130 citations). Guangyong Chen has collaborated with scholars based in China, Hong Kong and United States. Frequent co-authors include Pheng‐Ann Heng, Hanqun Cao, Daniel Cohen‐Or, Zhangyang Gao, Cheng Tan, Stan Z. Li, Hui Huang, Di Lin, Chang‐Yu Hsieh and Hang Zhao. Their work appears in journals such as Journal of the American Chemical Society, Nature Communications and SHILAP Revista de lepidopterología.
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.