Ke Tu
Impact in
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- Complex Network Analysis Techniques
- Artificial Intelligence top 5%
- Advanced Graph Neural Networks
- Topic Modeling
- Domain Adaptation and Few-Shot Learning
Papers in
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- Advanced Graph Neural Networks 7
- Topic Modeling 3
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- Recommender Systems and Techniques 5
- Co-authors
- Peng Cui (5 shared papers)Wenwu Zhu (4 shared papers)Xiao Wang (2 shared papers)Philip S. Yu (1 shared paper)Fei Wang (1 shared paper)Zhiqiang Zhang (1 shared paper)Jianxin Ma (1 shared paper)Daixin Wang (1 shared paper)
- Journals
- Frontiers of Computer Science (1 paper)Applied Mechanics and Materials (1 paper)Acta Horticulturae (1 paper)2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP) (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (1 paper)
- Partner nations
- ChinaUnited StatesCanada
In The Last Decade
Ke Tu
14 papers receiving 334 citations
Peers
Comparison fields: 5 of 54
- Statistical and Nonlinear Physics 151
- Artificial Intelligence 276
- Information Systems 100
- Computer Vision and Pattern Recognition 83
- Computational Mathematics 2
Countries citing papers authored by Ke Tu
This map shows the geographic impact of Ke Tu'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 Ke Tu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ke Tu more than expected).
Fields of papers citing papers by Ke Tu
This network shows the impact of papers produced by Ke Tu. 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 Ke Tu. The network helps show where Ke Tu may publish in the future.
Co-authors
The 25 scholars most cited alongside Ke Tu, 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 | 2018 | 126 | |
| 2 | 2018 | 102 | |
| 3 | 2021 | 35 | |
| 4 | 2020 | 25 | |
| 5 | 2019 | 24 | |
| 6 | 2006 | 8 | |
| 7 | 2023 | 6 | |
| 8 | 2024 | 6 | |
| 9 | 2023 | 3 | |
| 10 | 2024 | 2 | |
| 11 | 2022 | 2 | |
| 12 | 2014 | 1 | |
| 13 | 2015 | 1 | |
| 14 | 2022 | 1 | |
| 15 | 2023 | 0 | |
| 16 | 2025 | 0 |
About Ke Tu
Ke Tu is a scholar working on Artificial Intelligence, Information Systems, Computer Vision and Pattern Recognition, Computer Networks and Communications and Civil and Structural Engineering, having authored 16 papers that have together received 342 indexed citations. Recurring topics across this work include Advanced Graph Neural Networks (7 papers), Recommender Systems and Techniques (5 papers), Topic Modeling (3 papers), Video Surveillance and Tracking Methods (2 papers), Complex Network Analysis Techniques (2 papers), Advanced Image Fusion Techniques (1 paper), Infrastructure Maintenance and Monitoring (1 paper) and Medical Image Segmentation Techniques (1 paper). The work is most often cited by research in Statistical and Nonlinear Physics (151 citations), Artificial Intelligence (276 citations), Information Systems (100 citations), Computer Vision and Pattern Recognition (83 citations) and Computational Mathematics (2 citations). Ke Tu has collaborated with scholars based in China, United States and Canada. Frequent co-authors include Peng Cui, Wenwu Zhu, Xiao Wang, Philip S. Yu, Fei Wang, Zhiqiang Zhang, Jianxin Ma, Daixin Wang, Jian Pei and Qi Yuan. Their work appears in journals such as Frontiers of Computer Science, Applied Mechanics and Materials, Acta Horticulturae, 2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP) and Proceedings of the AAAI 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.