Guangyong Chen
- Molecular Biology
- Computer Vision and Pattern Recognition top 5%
- Computational Theory and Mathematics top 2%
- Materials Chemistry
- Artificial Intelligence top 10%
- Topics
- Machine Learning in Materials Science (9 papers)Computational Drug Discovery Methods (7 papers)Protein Structure and Dynamics (5 papers)
- Journals
- Journal of the American Chemical SocietyNature CommunicationsSHILAP Revista de lepidopterología
- 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
- Molecular Biology 444
- Computer Vision and Pattern Recognition 229
- Computational Theory and Mathematics 197
- Materials Chemistry 181
- Artificial Intelligence 171
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 of co-authors of Guangyong Chen
This figure shows the co-authorship network connecting the top 25 collaborators of Guangyong Chen. A scholar is included among the top collaborators of Guangyong Chen based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Guangyong Chen. Guangyong Chen is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 2 | |
| 3 | 2 | |
| 4 | 24 | |
| 5 | A Survey on Generative Diffusion Modelsbreakdown → | 192 |
| 6 | 0 | |
| 7 | 0 | |
| 8 | 2 | |
| 9 | 11 | |
| 10 | 9 | |
| 11 | 80 | |
| 12 | 1 | |
| 13 | 1 | |
| 14 | 60 | |
| 15 | 2 | |
| 16 | 4 | |
| 17 | 108 | |
| 18 | Learning to Aggregate Ordinal Labels by Maximizing Separating Width | 4 |
| 19 | 12 | |
| 20 | Research Progress in Bioactivity and Synthesis of β-caryophyllene and Its Derivatives | 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) and Protein Structure and Dynamics (5 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.