Kexiang Wang
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 10%
- Computer Graphics and Computer-Aided Design top 2%
- Computational Mechanics top 10%
- Information Systems
- Topics
- Topic Modeling (5 papers)Advanced Text Analysis Techniques (4 papers)Natural Language Processing Techniques (4 papers)
- Cited by
- Computer Graphics and Computer-Aided DesignArtificial IntelligenceComputer Vision and Pattern Recognition
- Journals
- IEEE Transactions on Visualization and Computer GraphicsRare & Special e-Zone (The Hong Kong University of Science and Technology)Proceedings of the AAAI Conference on Artificial Intelligence
- Partner nations
- ChinaUnited States
In The Last Decade
Kexiang Wang
9 papers receiving 351 citations
Peers
Comparison fields: 5 of 46
- Artificial Intelligence 245
- Computer Vision and Pattern Recognition 92
- Computer Graphics and Computer-Aided Design 87
- Computational Mechanics 84
- Information Systems 28
Countries citing papers authored by Kexiang Wang
This map shows the geographic impact of Kexiang Wang'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 Kexiang Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kexiang Wang more than expected).
Fields of papers citing papers by Kexiang Wang
This network shows the impact of papers produced by Kexiang Wang. 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 Kexiang Wang. The network helps show where Kexiang Wang may publish in the future.
Co-authorship network of co-authors of Kexiang Wang
This figure shows the co-authorship network connecting the top 25 collaborators of Kexiang Wang. A scholar is included among the top collaborators of Kexiang Wang 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 Kexiang Wang. Kexiang Wang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 8 | |
| 3 | 135 | |
| 4 | 9 | |
| 5 | 94 | |
| 6 | 46 | |
| 7 | 34 | |
| 8 | 34 | |
| 9 | 9 |
About Kexiang Wang
Kexiang Wang is a scholar working on Computer Graphics and Computer-Aided Design, Artificial Intelligence and Computational Mechanics, having authored 9 papers that have together received 370 indexed citations. Recurring topics across this work include Topic Modeling (5 papers), Advanced Text Analysis Techniques (4 papers) and Natural Language Processing Techniques (4 papers). The work is most often cited by research in Computer Graphics and Computer-Aided Design (87 citations), Artificial Intelligence (245 citations) and Computer Vision and Pattern Recognition (92 citations). Kexiang Wang has collaborated with scholars based in China and United States. Frequent co-authors include Baobao Chang, Zhifang Sui, Tianyu Liu, Lei Sha, Xin Li, Hong Qin, Bo Li, Bo Li, Bo Li and Paul R. Fisher. Their work appears in journals such as IEEE Transactions on Visualization and Computer Graphics, Rare & Special e-Zone (The Hong Kong University of Science and Technology) 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.