Yeon-Chang Lee
- Artificial Intelligence top 5%
- Information Systems top 5%
- Computer Vision and Pattern Recognition top 10%
- Statistical and Nonlinear Physics top 10%
- Management Science and Operations Research top 10%
- Co-authors
- Sang‐Wook KimDongwon LeeJongwuk LeeWonseok HwangSrijan KumarKijung ShinKyungsik HanKartik Sharma
- Topics
- Advanced Graph Neural Networks (19 papers)Recommender Systems and Techniques (18 papers)Complex Network Analysis Techniques (8 papers)
- Partner nations
- South KoreaUnited StatesChina
In The Last Decade
Yeon-Chang Lee
33 papers receiving 419 citations
Hit Papers
Peers
Comparison fields: 5 of 57
- Artificial Intelligence 266
- Information Systems 254
- Computer Vision and Pattern Recognition 131
- Statistical and Nonlinear Physics 71
- Management Science and Operations Research 52
Countries citing papers authored by Yeon-Chang Lee
This map shows the geographic impact of Yeon-Chang Lee'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 Yeon-Chang Lee with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yeon-Chang Lee more than expected).
Fields of papers citing papers by Yeon-Chang Lee
This network shows the impact of papers produced by Yeon-Chang Lee. 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 Yeon-Chang Lee. The network helps show where Yeon-Chang Lee may publish in the future.
Co-authorship network of co-authors of Yeon-Chang Lee
This figure shows the co-authorship network connecting the top 25 collaborators of Yeon-Chang Lee. A scholar is included among the top collaborators of Yeon-Chang Lee 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 Yeon-Chang Lee. Yeon-Chang Lee is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | A Survey of Graph Neural Networks for Social Recommender Systemsbreakdown → | 56 |
| 2 | 6 | |
| 3 | 1 | |
| 4 | 1 | |
| 5 | 0 | |
| 6 | 1 | |
| 7 | 10 | |
| 8 | 6 | |
| 9 | 9 | |
| 10 | 26 | |
| 11 | 2 | |
| 12 | 8 | |
| 13 | 7 | |
| 14 | 1 | |
| 15 | 15 | |
| 16 | 30 | |
| 17 | 4 | |
| 18 | 63 | |
| 19 | 12 | |
| 20 | 4 |
About Yeon-Chang Lee
Yeon-Chang Lee is a scholar working on Information Systems, Artificial Intelligence and Statistical and Nonlinear Physics, having authored 35 papers that have together received 429 indexed citations. Recurring topics across this work include Advanced Graph Neural Networks (19 papers), Recommender Systems and Techniques (18 papers) and Complex Network Analysis Techniques (8 papers). The work is most often cited by research in Information Systems (254 citations), Artificial Intelligence (266 citations) and Computational Mathematics (4 citations). Yeon-Chang Lee has collaborated with scholars based in South Korea, United States and China. Frequent co-authors include Sang‐Wook Kim, Dongwon Lee, Jongwuk Lee, Wonseok Hwang, Srijan Kumar, Kijung Shin, Kyungsik Han, Kartik Sharma, Jeongwhan Choi and Noseong Park. Their work appears in journals such as Expert Systems with Applications, ACM Computing Surveys and Information Sciences.
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.