Sha Yuan
Impact in
- Health Informatics top 10%
- Computer Science Applications top 10%
- Mobile Crowdsensing and Crowdsourcing
Papers in
-
- Topic Modeling 6
- Semantic Web and Ontologies 2
- Co-authors
- Zhou Shao (9 shared papers)Yongli Wang (7 shared papers)Jie Tang (4 shared papers)Xingxing Wei (2 shared papers)Wendy Hall (3 shared papers)Hang Su (1 shared paper)Jun Zhu (1 shared paper)Yu Zhang (1 shared paper)
- Journals
- IEEE Access (2 papers)Information Sciences (1 paper)Measurement (1 paper)Transactions on Emerging Telecommunications Technologies (1 paper)Electronics (1 paper)
- Partner nations
- ChinaUnited StatesUnited Kingdom
In The Last Decade
Sha Yuan
29 papers receiving 371 citations
Peers
Comparison fields: 5 of 80
- Health Informatics 15
- Computer Science Applications 40
- Artificial Intelligence 227
- Information Systems 107
- Computer Vision and Pattern Recognition 69
Countries citing papers authored by Sha Yuan
This map shows the geographic impact of Sha Yuan'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 Sha Yuan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sha Yuan more than expected).
Fields of papers citing papers by Sha Yuan
This network shows the impact of papers produced by Sha Yuan. 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 Sha Yuan. The network helps show where Sha Yuan may publish in the future.
Co-authors
The 25 scholars most cited alongside Sha Yuan, 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 31 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2019 | 78 | |
| 2 | 2019 | 75 | |
| 3 | 2021 | 38 | |
| 4 | 2021 | 34 | |
| 5 | 2022 | 29 | |
| 6 | 2020 | 21 | |
| 7 | 2020 | 20 | |
| 8 | Platelet-rich plasma in combination with adipose-derived stem cells promotes skin wound healing through activating Rho GTPase-mediated signaling pathway. | 2019 | 13 |
| 9 | 2018 | 10 | |
| 10 | 2020 | 8 | |
| 11 | 2024 | 7 | |
| 12 | 2017 | 6 | |
| 13 | 2021 | 6 | |
| 14 | 2016 | 5 | |
| 15 | 2015 | 5 | |
| 16 | 2023 | 4 | |
| 17 | 2016 | 4 | |
| 18 | 2015 | 4 | |
| 19 | 2020 | 4 | |
| 20 | 2016 | 3 |
About Sha Yuan
Sha Yuan is a scholar working on Artificial Intelligence, Information Systems, Computer Networks and Communications, Computer Vision and Pattern Recognition and Management Science and Operations Research, having authored 31 papers that have together received 391 indexed citations. Recurring topics across this work include Topic Modeling (6 papers), Caching and Content Delivery (4 papers), Data Quality and Management (3 papers), Hemodynamic Monitoring and Therapy (2 papers), scientometrics and bibliometrics research (2 papers), Complex Network Analysis Techniques (2 papers), Semantic Web and Ontologies (2 papers) and Cooperative Communication and Network Coding (2 papers). The work is most often cited by research in Health Informatics (15 citations), Computer Science Applications (40 citations), Artificial Intelligence (227 citations), Information Systems (107 citations) and Computer Vision and Pattern Recognition (69 citations). Sha Yuan has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Zhou Shao, Yongli Wang, Jie Tang, Xingxing Wei, Wendy Hall, Hang Su, Jun Zhu, Yu Zhang, Zhengxiao Du and Xiao Liu. Their work appears in journals such as IEEE Access, Information Sciences, Measurement, Transactions on Emerging Telecommunications Technologies and Electronics.
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.