Mingdong Ou
Impact in
-
- Complex Network Analysis Techniques
- Artificial Intelligence top 2%
- Advanced Graph Neural Networks
- Topic Modeling
Papers in
-
- Advanced Image and Video Retrieval Techniques 5
- Multimodal Machine Learning Applications 4
- Video Surveillance and Tracking Methods 3
- Image Retrieval and Classification Techniques 2
-
- Expert finding and Q&A systems 1
- Co-authors
- Peng Cui (7 shared papers)Wenwu Zhu (6 shared papers)Jian Pei (1 shared paper)Ziwei Zhang (1 shared paper)Daixin Wang (2 shared papers)Fei Wang (4 shared papers)Shiqiang Yang (2 shared papers)Shaowei Liu (1 shared paper)
- Journals
- IEEE Transactions on Multimedia (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (2 papers)International Conference on Artificial Intelligence (1 paper)
- Partner nations
- ChinaUnited StatesCanada
In The Last Decade
Mingdong Ou
9 papers receiving 1.0k citations
Mingdong Ou's Hit Papers
Peers
Comparison fields: 5 of 75
- Statistical and Nonlinear Physics 511
- Artificial Intelligence 687
- Computer Vision and Pattern Recognition 278
- Computational Mathematics 7
- Information Systems 198
Countries citing papers authored by Mingdong Ou
This map shows the geographic impact of Mingdong Ou'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 Mingdong Ou with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mingdong Ou more than expected).
Fields of papers citing papers by Mingdong Ou
This network shows the impact of papers produced by Mingdong Ou. 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 Mingdong Ou. The network helps show where Mingdong Ou may publish in the future.
Co-authors
The 15 scholars most cited alongside Mingdong Ou, 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 | Asymmetric Transitivity Preserving Graph Embedding Hit paper breakdown → | 2016 | 730 |
| 2 | 2011 | 95 | |
| 3 | 2015 | 75 | |
| 4 | Deep multimodal hashing with orthogonal regularization | 2015 | 74 |
| 5 | 2013 | 47 | |
| 6 | 2015 | 22 | |
| 7 | 2015 | 9 | |
| 8 | 2020 | 6 | |
| 9 | 2019 | 3 |
About Mingdong Ou
Mingdong Ou is a scholar working on Computer Vision and Pattern Recognition, Information Systems, Statistical and Nonlinear Physics, Artificial Intelligence and Management Science and Operations Research, having authored 9 papers that have together received 1.1k indexed citations. Recurring topics across this work include Advanced Image and Video Retrieval Techniques (5 papers), Multimodal Machine Learning Applications (4 papers), Video Surveillance and Tracking Methods (3 papers), Image Retrieval and Classification Techniques (2 papers), Complex Network Analysis Techniques (2 papers), Advanced Bandit Algorithms Research (2 papers), Reinforcement Learning in Robotics (1 paper) and Expert finding and Q&A systems (1 paper). The work is most often cited by research in Statistical and Nonlinear Physics (511 citations), Artificial Intelligence (687 citations), Computer Vision and Pattern Recognition (278 citations), Computational Mathematics (7 citations) and Information Systems (198 citations). Mingdong Ou has collaborated with scholars based in China, United States and Canada. Frequent co-authors include Peng Cui, Wenwu Zhu, Jian Pei, Ziwei Zhang, Daixin Wang, Fei Wang, Shiqiang Yang, Shaowei Liu, Lifeng Sun and Jun Wang. Their work appears in journals such as IEEE Transactions on Multimedia, Proceedings of the AAAI Conference on Artificial Intelligence and International Conference on Artificial Intelligence.
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.