Liangyue Li
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition top 5%
- Information Systems top 10%
- Statistical and Nonlinear Physics top 10%
- Computational Mechanics
- Topics
- Advanced Graph Neural Networks (10 papers)Recommender Systems and Techniques (9 papers)Topic Modeling (7 papers)
- Cited by
- Computer Vision and Pattern RecognitionStatistical and Nonlinear PhysicsComputer Science Applications
- Journals
- IEEE Transactions on Knowledge and Data EngineeringImage and Vision ComputingACM Transactions on Information Systems
- Partner nations
- United StatesChinaGermany
In The Last Decade
Liangyue Li
33 papers receiving 388 citations
Peers
Comparison fields: 5 of 71
- Artificial Intelligence 195
- Computer Vision and Pattern Recognition 155
- Information Systems 85
- Statistical and Nonlinear Physics 80
- Computational Mechanics 54
Countries citing papers authored by Liangyue Li
This map shows the geographic impact of Liangyue Li'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 Liangyue Li with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Liangyue Li more than expected).
Fields of papers citing papers by Liangyue Li
This network shows the impact of papers produced by Liangyue Li. 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 Liangyue Li. The network helps show where Liangyue Li may publish in the future.
Co-authorship network of co-authors of Liangyue Li
This figure shows the co-authorship network connecting the top 25 collaborators of Liangyue Li. A scholar is included among the top collaborators of Liangyue Li 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 Liangyue Li. Liangyue Li 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 | 5 | |
| 3 | 1 | |
| 4 | 7 | |
| 5 | 2 | |
| 6 | 2 | |
| 7 | 13 | |
| 8 | 6 | |
| 9 | 1 | |
| 10 | 9 | |
| 11 | 7 | |
| 12 | 9 | |
| 13 | 1 | |
| 14 | 9 | |
| 15 | 5 | |
| 16 | 5 | |
| 17 | 10 | |
| 18 | Predicting professions through probabilistic model under social context | 1 |
| 19 | 22 | |
| 20 | 1 |
About Liangyue Li
Liangyue Li is a scholar working on Computer Science Applications, Artificial Intelligence and Transportation, having authored 35 papers that have together received 392 indexed citations. Recurring topics across this work include Advanced Graph Neural Networks (10 papers), Recommender Systems and Techniques (9 papers) and Topic Modeling (7 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (155 citations), Statistical and Nonlinear Physics (80 citations) and Computer Science Applications (34 citations). Liangyue Li has collaborated with scholars based in United States, China and Germany. Frequent co-authors include Hanghang Tong, Yun Fu, Sheng Li, Nan Cao, Yu‐Ru Lin, Norbou Buchler, Sheng Li, Yun Fu, Kate Ehrlich and How Jing. Their work appears in journals such as IEEE Transactions on Knowledge and Data Engineering, Image and Vision Computing and ACM Transactions on Information Systems.
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.