Maolin Che
Impact in
- Computational Mathematics top 0.05%
- Tensor decomposition and applications
- Numerical Analysis top 5%
- Advanced Optimization Algorithms Research
Papers in
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- Tensor decomposition and applications 41
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- Matrix Theory and Algorithms 25
- Co-authors
- Yimin Wei (42 shared papers)Xuezhong Wang (15 shared papers)Liqun Qi (8 shared papers)Hong Yan (9 shared papers)Andrzej Cichocki (1 shared paper)Chaoqian Li (2 shared papers)Yonghe Liu (2 shared papers)Guofeng Zhang (1 shared paper)
In The Last Decade
Maolin Che
45 papers receiving 889 citations
Peers
Comparison fields: 5 of 53
- Computational Mathematics 758
- Numerical Analysis 164
- Computational Theory and Mathematics 482
- Computational Mechanics 236
- Statistical and Nonlinear Physics 128
Countries citing papers authored by Maolin Che
This map shows the geographic impact of Maolin Che'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 Maolin Che with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Maolin Che more than expected).
Fields of papers citing papers by Maolin Che
This network shows the impact of papers produced by Maolin Che. 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 Maolin Che. The network helps show where Maolin Che may publish in the future.
Co-authors
The 15 scholars most cited alongside Maolin Che, 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 48 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2015 | 124 | |
| 2 | 2018 | 86 | |
| 3 | 2019 | 58 | |
| 4 | 2020 | 52 | |
| 5 | 2020 | 49 | |
| 6 | 2017 | 40 | |
| 7 | 2019 | 36 | |
| 8 | 2020 | 36 | |
| 9 | 2019 | 29 | |
| 10 | 2018 | 29 | |
| 11 | 2021 | 26 | |
| 12 | 2021 | 25 | |
| 13 | 2019 | 24 | |
| 14 | 2022 | 23 | |
| 15 | 2022 | 22 | |
| 16 | 2019 | 20 | |
| 17 | 2016 | 19 | |
| 18 | 2022 | 19 | |
| 19 | 2019 | 17 | |
| 20 | 2020 | 14 |
About Maolin Che
Maolin Che is a scholar working on Computational Mathematics, Computational Theory and Mathematics, Computational Mechanics, Statistical and Nonlinear Physics and Signal Processing, having authored 48 papers that have together received 911 indexed citations. Recurring topics across this work include Tensor decomposition and applications (41 papers), Matrix Theory and Algorithms (25 papers), Sparse and Compressive Sensing Techniques (15 papers), Model Reduction and Neural Networks (8 papers), Blind Source Separation Techniques (7 papers), Advanced Neuroimaging Techniques and Applications (5 papers), Neural Networks and Applications (4 papers) and Power System Optimization and Stability (4 papers). The work is most often cited by research in Computational Mathematics (758 citations), Numerical Analysis (164 citations), Computational Theory and Mathematics (482 citations), Computational Mechanics (236 citations) and Statistical and Nonlinear Physics (128 citations). Maolin Che has collaborated with scholars based in China, Hong Kong and Japan. Frequent co-authors include Yimin Wei, Xuezhong Wang, Liqun Qi, Hong Yan, Andrzej Cichocki, Chaoqian Li, Yonghe Liu, Guofeng Zhang, Xi-Le Zhao and Changjiang Bu. Their work appears in journals such as Neurocomputing, Journal of Computational and Applied Mathematics, Journal of Scientific Computing, Computational Optimization and Applications and Numerical Algorithms.
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.