Keting Cen
Impact in
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- Complex Network Analysis Techniques
- Opinion Dynamics and Social Influence
- Artificial Intelligence top 10%
- Advanced Graph Neural Networks
- Topic Modeling
- Sentiment Analysis and Opinion Mining
Papers in
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- Advanced Graph Neural Networks 6
- Topic Modeling 1
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- Graph Theory and Algorithms 2
- Data Visualization and Analytics 1
- Generative Adversarial Networks and Image Synthesis 1
- Co-authors
- Xueqi Cheng (7 shared papers)Qi Cao (6 shared papers)Huawei Shen (4 shared papers)Wentao Ouyang (1 shared paper)Bingbing Xu (4 shared papers)Qi Cao (1 shared paper)Bing‐Bing Xu (1 shared paper)Wen Zheng (2 shared papers)
- Journals
- Neural Networks (2 papers)2022 International Joint Conference on Neural Networks (IJCNN) (1 paper)IEEE Conference Proceedings (1 paper)SSRN Electronic Journal (1 paper)
- Partner nations
- China
In The Last Decade
Keting Cen
8 papers receiving 248 citations
Peers
Comparison fields: 5 of 50
- Statistical and Nonlinear Physics 138
- Artificial Intelligence 165
- Transportation 21
- Computer Vision and Pattern Recognition 42
- Computational Mathematics 1
Countries citing papers authored by Keting Cen
This map shows the geographic impact of Keting Cen'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 Keting Cen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Keting Cen more than expected).
Fields of papers citing papers by Keting Cen
This network shows the impact of papers produced by Keting Cen. 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 Keting Cen. The network helps show where Keting Cen may publish in the future.
Co-authors
The 13 scholars most cited alongside Keting Cen, 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 | 2017 | 148 | |
| 2 | 2019 | 89 | |
| 3 | 2022 | 7 | |
| 4 | 2024 | 4 | |
| 5 | 2020 | 1 | |
| 6 | Towards Powerful Graph Neural Networks: Diversity Matters | 2021 | 1 |
| 7 | 2023 | 1 | |
| 8 | 2022 | 1 |
About Keting Cen
Keting Cen is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics, Information Systems and Cognitive Neuroscience, having authored 8 papers that have together received 252 indexed citations. Recurring topics across this work include Advanced Graph Neural Networks (6 papers), Complex Network Analysis Techniques (3 papers), Graph Theory and Algorithms (2 papers), Recommender Systems and Techniques (1 paper), Machine Learning in Materials Science (1 paper), Data Visualization and Analytics (1 paper), Generative Adversarial Networks and Image Synthesis (1 paper) and Topic Modeling (1 paper). The work is most often cited by research in Statistical and Nonlinear Physics (138 citations), Artificial Intelligence (165 citations), Transportation (21 citations), Computer Vision and Pattern Recognition (42 citations) and Computational Mathematics (1 citation). Keting Cen has collaborated with scholars based in China. Frequent co-authors include Xueqi Cheng, Qi Cao, Huawei Shen, Wentao Ouyang, Bingbing Xu, Huawei Shen, Qi Cao, Bing‐Bing Xu, Wen Zheng and Zhaohui Wang. Their work appears in journals such as Neural Networks, 2022 International Joint Conference on Neural Networks (IJCNN), IEEE Conference Proceedings and SSRN Electronic Journal.
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.