Suyu Ge
Impact in
- Information Systems top 5%
- Recommender Systems and Techniques
- Web Data Mining and Analysis
- Artificial Intelligence top 5%
- Topic Modeling
- Advanced Graph Neural Networks
- Sentiment Analysis and Opinion Mining
- Advanced Text Analysis Techniques
- Text and Document Classification Technologies
Papers in
-
- Topic Modeling 9
- Advanced Text Analysis Techniques 3
- Natural Language Processing Techniques 3
- Sentiment Analysis and Opinion Mining 3
- Advanced Graph Neural Networks 2
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- Recommender Systems and Techniques 3
- Co-authors
- Yongfeng Huang (6 shared papers)Tao Qi (6 shared papers)Chuhan Wu (6 shared papers)Fangzhao Wu (3 shared papers)Xing Xie (3 shared papers)Ying Yu (1 shared paper)Davy Weissenbacher (1 shared paper)Graciela Gonzalez‐Hernandez (1 shared paper)
- Journals
- Journal of the American Medical Informatics Association (1 paper)Ecotoxicology and Environmental Safety (1 paper)International Dental Journal (1 paper)
- Partner nations
- ChinaUnited StatesUnited Kingdom
In The Last Decade
Suyu Ge
10 papers receiving 252 citations
Peers
Comparison fields: 5 of 58
- Information Systems 180
- Artificial Intelligence 213
- Computer Vision and Pattern Recognition 44
- Family Practice 3
- Management Science and Operations Research 11
Countries citing papers authored by Suyu Ge
This map shows the geographic impact of Suyu Ge'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 Suyu Ge with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Suyu Ge more than expected).
Fields of papers citing papers by Suyu Ge
This network shows the impact of papers produced by Suyu Ge. 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 Suyu Ge. The network helps show where Suyu Ge may publish in the future.
Co-authors
The 25 scholars most cited alongside Suyu Ge, 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 | 2019 | 188 | |
| 2 | 2019 | 30 | |
| 3 | 2019 | 10 | |
| 4 | 2021 | 8 | |
| 5 | 2019 | 8 | |
| 6 | 2021 | 7 | |
| 7 | 2022 | 4 | |
| 8 | 2019 | 3 | |
| 9 | 2019 | 3 | |
| 10 | 2023 | 1 | |
| 11 | 2024 | 0 | |
| 12 | 2023 | 0 |
About Suyu Ge
Suyu Ge is a scholar working on Artificial Intelligence, Information Systems, Rheumatology, Molecular Biology and General Health Professions, having authored 12 papers that have together received 262 indexed citations. Recurring topics across this work include Topic Modeling (9 papers), Advanced Text Analysis Techniques (3 papers), Natural Language Processing Techniques (3 papers), Sentiment Analysis and Opinion Mining (3 papers), Recommender Systems and Techniques (3 papers), Advanced Graph Neural Networks (2 papers), Health Literacy and Information Accessibility (1 paper) and Bone and Dental Protein Studies (1 paper). The work is most often cited by research in Information Systems (180 citations), Artificial Intelligence (213 citations), Computer Vision and Pattern Recognition (44 citations), Family Practice (3 citations) and Management Science and Operations Research (11 citations). Suyu Ge has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Yongfeng Huang, Tao Qi, Chuhan Wu, Fangzhao Wu, Xing Xie, Ying Yu, Davy Weissenbacher, Graciela Gonzalez‐Hernandez, Sean Hennessy and Karen O’Connor. Their work appears in journals such as Journal of the American Medical Informatics Association, Ecotoxicology and Environmental Safety and International Dental Journal.
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.