Lirong Wu
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 5%
- Atmospheric Science top 10%
- Information Systems top 10%
- Statistical and Nonlinear Physics top 10%
- Topics
- Advanced Graph Neural Networks (12 papers)Topic Modeling (6 papers)Text and Document Classification Technologies (5 papers)
- Cited by
- Artificial IntelligenceComputer Vision and Pattern RecognitionStatistical and Nonlinear Physics
- Journals
- Scientific ReportsIEEE Transactions on Neural Networks and Learning SystemsIEEE Transactions on Knowledge and Data Engineering
- Partner nations
- ChinaUnited StatesGermany
In The Last Decade
Lirong Wu
33 papers receiving 820 citations
Hit Papers
Peers
Comparison fields: 5 of 102
- Artificial Intelligence 425
- Computer Vision and Pattern Recognition 237
- Atmospheric Science 106
- Information Systems 92
- Statistical and Nonlinear Physics 75
Countries citing papers authored by Lirong Wu
This map shows the geographic impact of Lirong Wu'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 Lirong Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lirong Wu more than expected).
Fields of papers citing papers by Lirong Wu
This network shows the impact of papers produced by Lirong Wu. 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 Lirong Wu. The network helps show where Lirong Wu may publish in the future.
Co-authorship network of co-authors of Lirong Wu
This figure shows the co-authorship network connecting the top 25 collaborators of Lirong Wu. A scholar is included among the top collaborators of Lirong Wu 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 Lirong Wu. Lirong Wu 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 | 1 | |
| 3 | 5 | |
| 4 | 1 | |
| 5 | 0 | |
| 6 | 2 | |
| 7 | 1 | |
| 8 | 1 | |
| 9 | 10 | |
| 10 | 14 | |
| 11 | 18 | |
| 12 | 81 | |
| 13 | 9 | |
| 14 | 169 | |
| 15 | Self-supervised on Graphs: Contrastive, Generative, or Predictive. | 12 |
| 16 | 20 | |
| 17 | Deep Manifold Computing and Visualization | 2 |
| 18 | 1 | |
| 19 | Optimization Model for the Layout of Pipeline | 0 |
| 20 | Establishment of a Certain Aero-Engine Starting Mathematical Model in Plateau Regions | 1 |
About Lirong Wu
Lirong Wu is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing, having authored 38 papers that have together received 828 indexed citations. Recurring topics across this work include Advanced Graph Neural Networks (12 papers), Topic Modeling (6 papers) and Text and Document Classification Technologies (5 papers). The work is most often cited by research in Artificial Intelligence (425 citations), Computer Vision and Pattern Recognition (237 citations) and Statistical and Nonlinear Physics (75 citations). Lirong Wu has collaborated with scholars based in China, United States and Germany. Frequent co-authors include Stan Z. Li, Zhangyang Gao, Cheng Tan, Haitao Lin, Jun Xia, Bozhen Hu, Yongjie Xu, Siyuan Li, Kejie Huang and Haibin Shen. Their work appears in journals such as Scientific Reports, IEEE Transactions on Neural Networks and Learning Systems and IEEE Transactions on Knowledge and Data Engineering.
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.