Guangyan Lin
Impact in
- Information Systems top 5%
- Recommender Systems and Techniques
- Artificial Intelligence top 5%
- Advanced Graph Neural Networks
- Topic Modeling
Papers in
-
- Topic Modeling 5
- Advanced Graph Neural Networks 3
- Semantic Web and Ontologies 1
- Natural Language Processing Techniques 1
-
- Recommender Systems and Techniques 2
- Co-authors
- Huobin Tan (6 shared papers)Xiyang Liu (2 shared papers)Ze Wang (1 shared paper)Long Liu (1 shared paper)Zhenxia Zhao (1 shared paper)Zhongxing Zhao (1 shared paper)Kungang Chai (1 shared paper)Qing Chang (1 shared paper)
- Journals
- Journal of Agricultural and Food Chemistry (1 paper)IEEE Access (1 paper)Journal of Intelligent & Fuzzy Systems (1 paper)Beijing Hangkong Hangtian Daxue xuebao (1 paper)
- Partner nations
- ChinaUnited States
In The Last Decade
Guangyan Lin
8 papers receiving 285 citations
Peers
Comparison fields: 5 of 69
- Information Systems 182
- Artificial Intelligence 208
- Computer Vision and Pattern Recognition 33
- Management Science and Operations Research 17
- Insect Science 16
Countries citing papers authored by Guangyan Lin
This map shows the geographic impact of Guangyan Lin'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 Guangyan Lin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Guangyan Lin more than expected).
Fields of papers citing papers by Guangyan Lin
This network shows the impact of papers produced by Guangyan Lin. 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 Guangyan Lin. The network helps show where Guangyan Lin may publish in the future.
Co-authors
The 11 scholars most cited alongside Guangyan Lin, 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 | 2020 | 185 | |
| 2 | 2017 | 43 | |
| 3 | 2021 | 41 | |
| 4 | 2018 | 16 | |
| 5 | 2019 | 2 | |
| 6 | 2020 | 2 | |
| 7 | Robust proximal support vector machine | 2007 | 1 |
| 8 | 2008 | 1 | |
| 9 | 2018 | 0 |
About Guangyan Lin
Guangyan Lin is a scholar working on Artificial Intelligence, Information Systems, Molecular Biology, Management Science and Operations Research and Computer Networks and Communications, having authored 9 papers that have together received 291 indexed citations. Recurring topics across this work include Topic Modeling (5 papers), Advanced Graph Neural Networks (3 papers), Recommender Systems and Techniques (2 papers), Data Quality and Management (2 papers), Semantic Web and Ontologies (1 paper), Natural Language Processing Techniques (1 paper), Face and Expression Recognition (1 paper) and Insect Utilization and Effects (1 paper). The work is most often cited by research in Information Systems (182 citations), Artificial Intelligence (208 citations), Computer Vision and Pattern Recognition (33 citations), Management Science and Operations Research (17 citations) and Insect Science (16 citations). Guangyan Lin has collaborated with scholars based in China and United States. Frequent co-authors include Huobin Tan, Xiyang Liu, Ze Wang, Long Liu, Zhenxia Zhao, Zhongxing Zhao, Kungang Chai, Qing Chang, Ze Wang and Linfeng Wang. Their work appears in journals such as Journal of Agricultural and Food Chemistry, IEEE Access, Journal of Intelligent & Fuzzy Systems and Beijing Hangkong Hangtian Daxue xuebao.
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.