Kan Guo
- Building and Construction top 1%
- Transportation top 1%
- Control and Systems Engineering top 5%
- Computer Vision and Pattern Recognition top 5%
- Computational Mechanics top 5%
- Co-authors
- Baocai YinJunbin GaoYanfeng SunYongli HuSean QianXiaowu ChenDongqing ZouKe Zhang
- Topics
- Traffic Prediction and Management Techniques (9 papers)3D Shape Modeling and Analysis (8 papers)Computer Graphics and Visualization Techniques (6 papers)
- Journals
- ACM Transactions on GraphicsIEEE Transactions on Intelligent Transportation SystemsNeurocomputing
- Partner nations
- ChinaAustraliaUnited States
In The Last Decade
Kan Guo
18 papers receiving 832 citations
Hit Papers
Peers
Comparison fields: 5 of 84
- Building and Construction 495
- Transportation 355
- Control and Systems Engineering 241
- Computer Vision and Pattern Recognition 175
- Computational Mechanics 163
Countries citing papers authored by Kan Guo
This map shows the geographic impact of Kan Guo'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 Kan Guo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kan Guo more than expected).
Fields of papers citing papers by Kan Guo
This network shows the impact of papers produced by Kan Guo. 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 Kan Guo. The network helps show where Kan Guo may publish in the future.
Co-authorship network of co-authors of Kan Guo
This figure shows the co-authorship network connecting the top 25 collaborators of Kan Guo. A scholar is included among the top collaborators of Kan Guo 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 Kan Guo. Kan Guo is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 2 | |
| 4 | 7 | |
| 5 | 8 | |
| 6 | 9 | |
| 7 | 6 | |
| 8 | 64 | |
| 9 | 139 | |
| 10 | Optimized Graph Convolution Recurrent Neural Network for Traffic Predictionbreakdown → | 268 |
| 11 | 131 | |
| 12 | 4 | |
| 13 | 17 | |
| 14 | 3 | |
| 15 | 2 | |
| 16 | 1 | |
| 17 | 153 | |
| 18 | 2 | |
| 19 | 41 | |
| 20 | 1 |
About Kan Guo
Kan Guo is a scholar working on Computer Graphics and Computer-Aided Design, Transportation and Building and Construction, having authored 20 papers that have together received 858 indexed citations. Recurring topics across this work include Traffic Prediction and Management Techniques (9 papers), 3D Shape Modeling and Analysis (8 papers) and Computer Graphics and Visualization Techniques (6 papers). The work is most often cited by research in Transportation (355 citations), Building and Construction (495 citations) and Computer Graphics and Computer-Aided Design (90 citations). Kan Guo has collaborated with scholars based in China, Australia and United States. Frequent co-authors include Baocai Yin, Junbin Gao, Yanfeng Sun, Yongli Hu, Sean Qian, Xiaowu Chen, Dongqing Zou, Ke Zhang, Hao Liu and Qiang Fu. Their work appears in journals such as ACM Transactions on Graphics, IEEE Transactions on Intelligent Transportation Systems and Neurocomputing.
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.