Shaofu Yang
Impact in
- Computer Networks and Communications top 0.5%
- Neural Networks Stability and Synchronization
- Distributed Control Multi-Agent Systems
- Nonlinear Dynamics and Pattern Formation
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- stochastic dynamics and bifurcation
Papers in
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- Distributed Control Multi-Agent Systems 32
- Neural Networks Stability and Synchronization 23
- Nonlinear Dynamics and Pattern Formation 8
- Cooperative Communication and Network Coding 7
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- stochastic dynamics and bifurcation 8
In The Last Decade
Shaofu Yang
48 papers receiving 1.9k citations
Peers
Comparison fields: 5 of 62
- Computer Networks and Communications 1.6k
- Statistical and Nonlinear Physics 435
- Control and Systems Engineering 368
- Computational Theory and Mathematics 190
- Artificial Intelligence 334
Countries citing papers authored by Shaofu Yang
This map shows the geographic impact of Shaofu Yang'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 Shaofu Yang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shaofu Yang more than expected).
Fields of papers citing papers by Shaofu Yang
This network shows the impact of papers produced by Shaofu Yang. 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 Shaofu Yang. The network helps show where Shaofu Yang may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Shaofu Yang, 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 | 1 | |
| 2 | 2025 | 2 | |
| 3 | 2025 | 1 | |
| 4 | 2024 | 2 | |
| 5 | 2024 | 0 | |
| 6 | 2024 | 1 | |
| 7 | 2024 | 0 | |
| 8 | 2024 | 1 | |
| 9 | 2023 | 6 | |
| 10 | 2023 | 9 | |
| 11 | 2023 | 3 | |
| 12 | 2022 | 19 | |
| 13 | 2022 | 10 | |
| 14 | 2022 | 12 | |
| 15 | 2022 | 14 | |
| 16 | 2021 | 17 | |
| 17 | 2019 | 17 | |
| 18 | 2018 | 66 | |
| 19 | 2018 | 58 | |
| 20 | 2016 | 53 |
About Shaofu Yang
Shaofu Yang is a scholar working on Computer Networks and Communications, Statistical and Nonlinear Physics, Public Health, Environmental and Occupational Health, Management Science and Operations Research and Computational Theory and Mathematics, having authored 51 papers that have together received 1.9k indexed citations. Recurring topics across this work include Distributed Control Multi-Agent Systems (32 papers), Neural Networks Stability and Synchronization (23 papers), Mathematical and Theoretical Epidemiology and Ecology Models (12 papers), stochastic dynamics and bifurcation (8 papers), Nonlinear Dynamics and Pattern Formation (8 papers), Cooperative Communication and Network Coding (7 papers), Advanced Memory and Neural Computing (4 papers) and Stochastic Gradient Optimization Techniques (4 papers). The work is most often cited by research in Computer Networks and Communications (1.6k citations), Statistical and Nonlinear Physics (435 citations), Control and Systems Engineering (368 citations), Computational Theory and Mathematics (190 citations) and Artificial Intelligence (334 citations). Shaofu Yang has collaborated with scholars based in China, Hong Kong and Qatar. Frequent co-authors include Jun Wang, Qingshan Liu, Zhenyuan Guo, Tingwen Huang, Yiguang Hong, Shuqing Gong, Jinde Cao, Wenying Xu, Jianquan Lu and Xiaoxuan Wang. Their work appears in journals such as Neural Networks, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Automatic Control, IEEE Transactions on Systems Man and Cybernetics Systems and IEEE Transactions on Network Science and Engineering.
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.