Beibei Wang
- Artificial Intelligence
- Computer Vision and Pattern Recognition top 10%
- Computer Networks and Communications
- Control and Systems Engineering
- Ecology
- Topics
- Advanced Graph Neural Networks (11 papers)Domain Adaptation and Few-Shot Learning (4 papers)Graph Theory and Algorithms (3 papers)
- Cited by
- Computer Vision and Pattern RecognitionArtificial IntelligenceIndustrial and Manufacturing Engineering
- Journals
- IEEE Transactions on Pattern Analysis and Machine IntelligenceJournal of Hazardous MaterialsPlant and Soil
- Partner nations
- ChinaUnited KingdomUnited States
In The Last Decade
Beibei Wang
21 papers receiving 279 citations
Hit Papers
Peers
Comparison fields: 5 of 93
- Artificial Intelligence 91
- Computer Vision and Pattern Recognition 68
- Computer Networks and Communications 35
- Control and Systems Engineering 33
- Ecology 19
Countries citing papers authored by Beibei Wang
This map shows the geographic impact of Beibei 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 Beibei Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Beibei Wang more than expected).
Fields of papers citing papers by Beibei Wang
This network shows the impact of papers produced by Beibei 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 Beibei Wang. The network helps show where Beibei Wang may publish in the future.
Co-authorship network of co-authors of Beibei Wang
This figure shows the co-authorship network connecting the top 25 collaborators of Beibei Wang. A scholar is included among the top collaborators of Beibei 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 Beibei Wang. Beibei 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 | 2 | |
| 3 | 1 | |
| 4 | 13 | |
| 5 | 2 | |
| 6 | 7 | |
| 7 | 1 | |
| 8 | 4 | |
| 9 | 3 | |
| 10 | MGLNN: Semi-supervised learning via Multiple Graph Cooperative Learning Neural Networksbreakdown → | 142 |
| 11 | 10 | |
| 12 | 9 | |
| 13 | 9 | |
| 14 | 17 | |
| 15 | Graph Mask Convolutional Network. | 1 |
| 16 | 4 | |
| 17 | 11 | |
| 18 | 18 | |
| 19 | 9 | |
| 20 | 14 |
About Beibei Wang
Beibei Wang is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics and Computer Vision and Pattern Recognition, having authored 22 papers that have together received 285 indexed citations. Recurring topics across this work include Advanced Graph Neural Networks (11 papers), Domain Adaptation and Few-Shot Learning (4 papers) and Graph Theory and Algorithms (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (68 citations), Artificial Intelligence (91 citations) and Industrial and Manufacturing Engineering (18 citations). Beibei Wang has collaborated with scholars based in China, United Kingdom and United States. Frequent co-authors include Bo Jiang, Bin Luo, Jin Tang, Chaohe Huangfu, Terry Bennett, Harm Askes, Yuangong Sun, Peihao Zhao, Hengguo Yu and Haiyun Xu. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Journal of Hazardous Materials and Plant and Soil.
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.