Yiyang Gu
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 10%
- Information Systems top 10%
- Statistical and Nonlinear Physics top 10%
- Molecular Biology
- Topics
- Advanced Graph Neural Networks (9 papers)Recommender Systems and Techniques (4 papers)Text and Document Classification Technologies (3 papers)
- Cited by
- Artificial IntelligenceComputer Vision and Pattern RecognitionStatistical and Nonlinear Physics
- Journals
- IEEE Transactions on Pattern Analysis and Machine IntelligenceIEEE Transactions on Neural Networks and Learning SystemsNeural Networks
- Partner nations
- ChinaUnited StatesHong Kong
In The Last Decade
Yiyang Gu
14 papers receiving 333 citations
Hit Papers
Peers
Comparison fields: 5 of 71
- Artificial Intelligence 230
- Computer Vision and Pattern Recognition 86
- Information Systems 47
- Statistical and Nonlinear Physics 44
- Molecular Biology 28
Countries citing papers authored by Yiyang Gu
This map shows the geographic impact of Yiyang Gu'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 Yiyang Gu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yiyang Gu more than expected).
Fields of papers citing papers by Yiyang Gu
This network shows the impact of papers produced by Yiyang Gu. 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 Yiyang Gu. The network helps show where Yiyang Gu may publish in the future.
Co-authorship network of co-authors of Yiyang Gu
This figure shows the co-authorship network connecting the top 25 collaborators of Yiyang Gu. A scholar is included among the top collaborators of Yiyang Gu 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 Yiyang Gu. Yiyang Gu 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 | 2 | |
| 3 | 1 | |
| 4 | 2 | |
| 5 | A Comprehensive Survey on Deep Graph Representation Learningbreakdown → | 138 |
| 6 | 15 | |
| 7 | 7 | |
| 8 | 13 | |
| 9 | 45 | |
| 10 | 13 | |
| 11 | 4 | |
| 12 | 52 | |
| 13 | 44 | |
| 14 | 4 | |
| 15 | 1 |
About Yiyang Gu
Yiyang Gu is a scholar working on Artificial Intelligence, Transportation and Health Information Management, having authored 15 papers that have together received 341 indexed citations. Recurring topics across this work include Advanced Graph Neural Networks (9 papers), Recommender Systems and Techniques (4 papers) and Text and Document Classification Technologies (3 papers). The work is most often cited by research in Artificial Intelligence (230 citations), Computer Vision and Pattern Recognition (86 citations) and Statistical and Nonlinear Physics (44 citations). Yiyang Gu has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Xiao Luo, Wei Ju, Ming Zhang, Yifan Wang, Yifang Qin, Yusheng Zhao, Zhiping Xiao, Zheng Fang, Fang Sun and Junwei Yang. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Neural Networks and Learning Systems and Neural Networks.
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.