Ling Ding
Impact in
- Artificial Intelligence top 10%
- Advanced Clustering Algorithms Research
- Reinforcement Learning in Robotics
- Advanced Graph Neural Networks
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- Face and Expression Recognition
Papers in
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- Advanced Clustering Algorithms Research 11
- Advanced Graph Neural Networks 6
- Text and Document Classification Technologies 3
- Reinforcement Learning in Robotics 3
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- Face and Expression Recognition 6
- Co-authors
- Shifei Ding (29 shared papers)Di Jin (3 shared papers)Xiao Xu (5 shared papers)Lili Guo (11 shared papers)Jian Zhang (5 shared papers)Lijuan Wang (4 shared papers)Yanru Wang (4 shared papers)Lili Guo (3 shared papers)
In The Last Decade
Ling Ding
39 papers receiving 367 citations
Ling Ding's Hit Papers
Peers
Comparison fields: 5 of 87
- Artificial Intelligence 177
- Computer Vision and Pattern Recognition 94
- Statistical and Nonlinear Physics 53
- Signal Processing 30
- Cognitive Neuroscience 54
Countries citing papers authored by Ling Ding
This map shows the geographic impact of Ling Ding'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 Ling Ding with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ling Ding more than expected).
Fields of papers citing papers by Ling Ding
This network shows the impact of papers produced by Ling Ding. 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 Ling Ding. The network helps show where Ling Ding may publish in the future.
Co-authors
The 25 scholars most cited alongside Ling Ding, 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 43 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2005 | 52 | |
| 2 | Survey of spectral clustering based on graph theory Hit paper breakdown → | 2024 | 49 |
| 3 | 2022 | 37 | |
| 4 | 2023 | 26 | |
| 5 | 2023 | 22 | |
| 6 | 2013 | 16 | |
| 7 | 2020 | 14 | |
| 8 | 2023 | 13 | |
| 9 | 2022 | 12 | |
| 10 | 2023 | 11 | |
| 11 | 2022 | 9 | |
| 12 | 2020 | 9 | |
| 13 | 2023 | 8 | |
| 14 | 2023 | 7 | |
| 15 | 2023 | 7 | |
| 16 | Review of Energy Storage System in Electric Power System | 2011 | 6 |
| 17 | 2024 | 6 | |
| 18 | 2022 | 6 | |
| 19 | 2023 | 6 | |
| 20 | 2023 | 5 |
About Ling Ding
Ling Ding is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics, Urban Studies and Signal Processing, having authored 43 papers that have together received 371 indexed citations. Recurring topics across this work include Advanced Clustering Algorithms Research (11 papers), Complex Network Analysis Techniques (10 papers), Face and Expression Recognition (6 papers), Advanced Graph Neural Networks (6 papers), Advanced Computing and Algorithms (5 papers), Data Management and Algorithms (3 papers), Text and Document Classification Technologies (3 papers) and Reinforcement Learning in Robotics (3 papers). The work is most often cited by research in Artificial Intelligence (177 citations), Computer Vision and Pattern Recognition (94 citations), Statistical and Nonlinear Physics (53 citations), Signal Processing (30 citations) and Cognitive Neuroscience (54 citations). Ling Ding has collaborated with scholars based in China, Singapore and Taiwan. Frequent co-authors include Shifei Ding, Di Jin, Xiao Xu, Lili Guo, Jian Zhang, Lijuan Wang, Yanru Wang, Lili Guo, S. Salinari and Laura Astolfi. Their work appears in journals such as International Journal of Machine Learning and Cybernetics, Pattern Recognition, Information Sciences, Soft Computing and IEEE Transactions on Neural Networks and Learning Systems.
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.