Cuiying Huo
Impact in
- Artificial Intelligence top 5%
- Advanced Graph Neural Networks
- Topic Modeling
- Domain Adaptation and Few-Shot Learning
- Privacy-Preserving Technologies in Data
- Text and Document Classification Technologies
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- Complex Network Analysis Techniques
Papers in
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- Advanced Graph Neural Networks 6
- Topic Modeling 3
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- Recommender Systems and Techniques 3
- Co-authors
- Di Jin (8 shared papers)Liang Yang (1 shared paper)Dongxiao He (7 shared papers)Lingfei Wu (3 shared papers)Zhizhi Yu (3 shared papers)Xiao Wang (1 shared paper)Rui Wang (1 shared paper)Jiawei Han (1 shared paper)
- Journals
- ACM Transactions on Intelligent Systems and Technology (1 paper)IEEE Transactions on Neural Networks and Learning Systems (1 paper)ACM Transactions on Information Systems (1 paper)Neural Information Processing Systems (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (3 papers)
- Partner nations
- ChinaUnited StatesHong Kong
In The Last Decade
Cuiying Huo
10 papers receiving 257 citations
Peers
Comparison fields: 5 of 42
- Artificial Intelligence 218
- Statistical and Nonlinear Physics 73
- Information Systems 76
- Computer Vision and Pattern Recognition 45
- Computational Mathematics 1
Countries citing papers authored by Cuiying Huo
This map shows the geographic impact of Cuiying Huo'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 Cuiying Huo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Cuiying Huo more than expected).
Fields of papers citing papers by Cuiying Huo
This network shows the impact of papers produced by Cuiying Huo. 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 Cuiying Huo. The network helps show where Cuiying Huo may publish in the future.
Co-authors
The 25 scholars most cited alongside Cuiying Huo, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2021 | 136 | |
| 2 | Universal Graph Convolutional Networks | 2021 | 45 |
| 3 | 2023 | 32 | |
| 4 | 2023 | 19 | |
| 5 | 2023 | 12 | |
| 6 | 2024 | 5 | |
| 7 | 2024 | 4 | |
| 8 | 2025 | 3 | |
| 9 | 2024 | 1 | |
| 10 | 2025 | 1 |
About Cuiying Huo
Cuiying Huo is a scholar working on Artificial Intelligence, Information Systems, Sociology and Political Science, Computer Vision and Pattern Recognition and Communication, having authored 10 papers that have together received 258 indexed citations. Recurring topics across this work include Advanced Graph Neural Networks (6 papers), Topic Modeling (3 papers), Recommender Systems and Techniques (3 papers), Graph Theory and Algorithms (2 papers), Knowledge Management and Sharing (1 paper), Access Control and Trust (1 paper), Socioeconomic Development in MENA (1 paper) and Bioinformatics and Genomic Networks (1 paper). The work is most often cited by research in Artificial Intelligence (218 citations), Statistical and Nonlinear Physics (73 citations), Information Systems (76 citations), Computer Vision and Pattern Recognition (45 citations) and Computational Mathematics (1 citation). Cuiying Huo has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Di Jin, Liang Yang, Dongxiao He, Lingfei Wu, Zhizhi Yu, Xiao Wang, Rui Wang, Jiawei Han, Yawen Li and Tie Qiu. Their work appears in journals such as ACM Transactions on Intelligent Systems and Technology, IEEE Transactions on Neural Networks and Learning Systems, ACM Transactions on Information Systems, Neural Information Processing Systems 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.