Guangquan Cheng
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 5%
- Control and Systems Engineering top 10%
- Aerospace Engineering top 10%
- Computer Networks and Communications top 10%
- Co-authors
- Zhong LiuChangjun FanCunchao ZhuYan ZhangMuhao ChenYanghe FengKuihua HuangJincai Huang
- Topics
- Reinforcement Learning in Robotics (8 papers)Complex Network Analysis Techniques (7 papers)Advanced Graph Neural Networks (6 papers)
- Cited by
- Artificial IntelligenceComputer Vision and Pattern RecognitionIndustrial and Manufacturing Engineering
- Partner nations
- ChinaUnited StatesFrance
In The Last Decade
Guangquan Cheng
40 papers receiving 683 citations
Peers
Comparison fields: 5 of 80
- Artificial Intelligence 340
- Computer Vision and Pattern Recognition 160
- Control and Systems Engineering 113
- Aerospace Engineering 95
- Computer Networks and Communications 89
Countries citing papers authored by Guangquan Cheng
This map shows the geographic impact of Guangquan Cheng'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 Guangquan Cheng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Guangquan Cheng more than expected).
Fields of papers citing papers by Guangquan Cheng
This network shows the impact of papers produced by Guangquan Cheng. 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 Guangquan Cheng. The network helps show where Guangquan Cheng may publish in the future.
Co-authorship network of co-authors of Guangquan Cheng
This figure shows the co-authorship network connecting the top 25 collaborators of Guangquan Cheng. A scholar is included among the top collaborators of Guangquan Cheng based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Guangquan Cheng. Guangquan Cheng is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 30 | |
| 3 | 0 | |
| 4 | 7 | |
| 5 | 9 | |
| 6 | 2 | |
| 7 | 25 | |
| 8 | 63 | |
| 9 | 153 | |
| 10 | 3 | |
| 11 | 5 | |
| 12 | 0 | |
| 13 | 21 | |
| 14 | 13 | |
| 15 | 81 | |
| 16 | 15 | |
| 17 | 18 | |
| 18 | 9 | |
| 19 | 13 | |
| 20 | A New Approach for Interesting Local Saliency Features Definition and Its Application to Remote Sensing Imagery Retrieval | 2 |
About Guangquan Cheng
Guangquan Cheng is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics and Management Science and Operations Research, having authored 43 papers that have together received 703 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (8 papers), Complex Network Analysis Techniques (7 papers) and Advanced Graph Neural Networks (6 papers). The work is most often cited by research in Artificial Intelligence (340 citations), Computer Vision and Pattern Recognition (160 citations) and Industrial and Manufacturing Engineering (67 citations). Guangquan Cheng has collaborated with scholars based in China, United States and France. Frequent co-authors include Zhong Liu, Changjun Fan, Cunchao Zhu, Yan Zhang, Muhao Chen, Yanghe Feng, Kuihua Huang, Jincai Huang, Jianmai Shi and Zhihao Luo. Their work appears in journals such as IEEE Access, Information Sciences and IEEE Transactions on Cybernetics.
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.