Siyu Yi
Impact in
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- Advanced Graph Neural Networks
- Domain Adaptation and Few-Shot Learning
Papers in
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- Advanced Graph Neural Networks 8
- Machine Learning and Algorithms 3
- Co-authors
- Yongdao Zhou (10 shared papers)Wei Ju (11 shared papers)Xiao Luo (6 shared papers)Ming Zhang (8 shared papers)Yifang Qin (6 shared papers)Xiangcheng Pan (6 shared papers)Luchen Liu (3 shared papers)Zhujun Huang (2 shared papers)
- Journals
- Chemical Communications (2 papers)Annals of Nuclear Energy (2 papers)IEEE Transactions on Multimedia (2 papers)Applied Surface Science (1 paper)ACM Transactions on Multimedia Computing Communications and Applications (1 paper)
- Partner nations
- ChinaUnited StatesHong Kong
In The Last Decade
Siyu Yi
27 papers receiving 233 citations
Peers
Comparison fields: 5 of 59
- Transportation 19
- Artificial Intelligence 66
- Building and Construction 22
- Computer Vision and Pattern Recognition 31
- Management Science and Operations Research 18
Countries citing papers authored by Siyu Yi
This map shows the geographic impact of Siyu Yi'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 Siyu Yi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Siyu Yi more than expected).
Fields of papers citing papers by Siyu Yi
This network shows the impact of papers produced by Siyu Yi. 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 Siyu Yi. The network helps show where Siyu Yi may publish in the future.
Co-authors
The 25 scholars most cited alongside Siyu Yi, 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 33 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2022 | 36 | |
| 2 | 2024 | 34 | |
| 3 | 2023 | 25 | |
| 4 | 2025 | 20 | |
| 5 | 2023 | 15 | |
| 6 | 2025 | 14 | |
| 7 | 2024 | 12 | |
| 8 | 2018 | 11 | |
| 9 | 2023 | 9 | |
| 10 | 2024 | 8 | |
| 11 | 2020 | 8 | |
| 12 | 2022 | 6 | |
| 13 | 2024 | 6 | |
| 14 | 2025 | 4 | |
| 15 | 2021 | 4 | |
| 16 | 2025 | 3 | |
| 17 | 2021 | 3 | |
| 18 | 2021 | 2 | |
| 19 | 2025 | 2 | |
| 20 | 2025 | 2 |
About Siyu Yi
Siyu Yi is a scholar working on Artificial Intelligence, Materials Chemistry, Management Science and Operations Research, Renewable Energy, Sustainability and the Environment and Organic Chemistry, having authored 33 papers that have together received 234 indexed citations. Recurring topics across this work include Advanced Graph Neural Networks (8 papers), Optimal Experimental Design Methods (5 papers), CO2 Reduction Techniques and Catalysts (5 papers), Ammonia Synthesis and Nitrogen Reduction (4 papers), Machine Learning and Algorithms (3 papers), Manufacturing Process and Optimization (3 papers), Statistical Methods and Inference (3 papers) and Advanced Multi-Objective Optimization Algorithms (3 papers). The work is most often cited by research in Transportation (19 citations), Artificial Intelligence (66 citations), Building and Construction (22 citations), Computer Vision and Pattern Recognition (31 citations) and Management Science and Operations Research (18 citations). Siyu Yi has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Yongdao Zhou, Wei Ju, Xiao Luo, Ming Zhang, Yifang Qin, Xiangcheng Pan, Luchen Liu, Zhujun Huang, Yuan Jiang and Zhiping Xiao. Their work appears in journals such as Chemical Communications, Annals of Nuclear Energy, IEEE Transactions on Multimedia, Applied Surface Science and ACM Transactions on Multimedia Computing Communications and Applications.
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.