Ding Zhou
Impact in
- Computational Mathematics top 5%
-
- Complex Network Analysis Techniques
- Opinion Dynamics and Social Influence
Papers in
-
- Complex Network Analysis Techniques 5
- Opinion Dynamics and Social Influence 3
-
- Neural Networks and Applications 2
- Advanced Text Analysis Techniques 1
- Co-authors
- Hongyuan Zha (6 shared papers)Chris Ding (1 shared paper)Xiaofeng He (1 shared paper)C. Lee Giles (5 shared papers)Eren Manavoglu (1 shared paper)Jia Li (1 shared paper)Isaac G. Councill (1 shared paper)Şeyda Ertekin (1 shared paper)
- Journals
- Cell Reports (1 paper)Machine Learning (1 paper)Measurement Science and Technology (1 paper)OpenMETU (Middle East Technical University) (1 paper)Neural Information Processing Systems (1 paper)
- Partner nations
- United StatesChina
In The Last Decade
Ding Zhou
13 papers receiving 896 citations
Peers
Comparison fields: 5 of 92
- Computational Mathematics 19
- Statistical and Nonlinear Physics 298
- Computer Vision and Pattern Recognition 333
- Artificial Intelligence 395
- Signal Processing 131
Countries citing papers authored by Ding Zhou
This map shows the geographic impact of Ding Zhou'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 Ding Zhou with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ding Zhou more than expected).
Fields of papers citing papers by Ding Zhou
This network shows the impact of papers produced by Ding Zhou. 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 Ding Zhou. The network helps show where Ding Zhou may publish in the future.
Co-authors
The 25 scholars most cited alongside Ding Zhou, 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 | 2006 | 472 | |
| 2 | 2007 | 165 | |
| 3 | 2006 | 151 | |
| 4 | 2006 | 60 | |
| 5 | 2007 | 41 | |
| 6 | 2021 | 24 | |
| 7 | 2007 | 12 | |
| 8 | 2005 | 5 | |
| 9 | 2021 | 3 | |
| 10 | A zero-inflated gamma model for post-deconvolved calcium imaging traces | 2020 | 1 |
| 11 | Learning identifiable and interpretable latent models of high-dimensional neural activity using pi-VAE | 2020 | 1 |
| 12 | 2025 | 1 | |
| 13 | 2021 | 1 |
About Ding Zhou
Ding Zhou is a scholar working on Statistical and Nonlinear Physics, Artificial Intelligence, Management Science and Operations Research, Cognitive Neuroscience and Transportation, having authored 13 papers that have together received 937 indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (5 papers), Opinion Dynamics and Social Influence (3 papers), Human Mobility and Location-Based Analysis (3 papers), Neural dynamics and brain function (3 papers), Neural Networks and Applications (2 papers), Advanced Text Analysis Techniques (1 paper), Video Surveillance and Tracking Methods (1 paper) and Face and Expression Recognition (1 paper). The work is most often cited by research in Computational Mathematics (19 citations), Statistical and Nonlinear Physics (298 citations), Computer Vision and Pattern Recognition (333 citations), Artificial Intelligence (395 citations) and Signal Processing (131 citations). Ding Zhou has collaborated with scholars based in United States and China. Frequent co-authors include Hongyuan Zha, Chris Ding, Xiaofeng He, C. Lee Giles, Eren Manavoglu, Jia Li, Isaac G. Councill, Şeyda Ertekin, Liam Paninski and Ian Kinsella. Their work appears in journals such as Cell Reports, Machine Learning, Measurement Science and Technology, OpenMETU (Middle East Technical University) and Neural Information Processing 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.