Sungchul Kim
- Artificial Intelligence top 1%
- Topic Modeling 17
- Advanced Graph Neural Networks 17
- Natural Language Processing Techniques 10
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- Complex Network Analysis Techniques 17
- Health Informatics top 10%
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- Data Visualization and Analytics 8
- Video Analysis and Summarization 7
- Information Systems top 5%
- Recommender Systems and Techniques 11
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- Time Series Analysis and Forecasting 9
- Co-authors
- Ryan A. RossiEunyee KohNesreen K. AhmedJohn Boaz LeeGiang NguyenHwanjo YuRuiyi ZhangAnup Rao
- Journals
- SHILAP Revista de lepidopterología (1 paper)BMC Bioinformatics (1 paper)Information Sciences (3 papers)
- Partner nations
- United StatesSouth KoreaChina
In The Last Decade
Sungchul Kim
83 papers receiving 1.4k citations
Hit Papers
Peers
Comparison fields: 5 of 139
- Artificial Intelligence 943
- Statistical and Nonlinear Physics 360
- Health Informatics 21
- Computer Vision and Pattern Recognition 304
- Information Systems 237
Countries citing papers authored by Sungchul Kim
This map shows the geographic impact of Sungchul Kim'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 Sungchul Kim with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sungchul Kim more than expected).
Fields of papers citing papers by Sungchul Kim
This network shows the impact of papers produced by Sungchul Kim. 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 Sungchul Kim. The network helps show where Sungchul Kim may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Sungchul Kim, 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 | 2025 | 0 | |
| 2 | 2025 | 0 | |
| 3 | 2025 | 3 | |
| 4 | 2025 | 0 | |
| 5 | 2024 | 0 | |
| 6 | 2024 | 1 | |
| 7 | 2024 | 6 | |
| 8 | 2024 | 3 | |
| 9 | 2024 | 1 | |
| 10 | 2024 | 1 | |
| 11 | 2024 | 3 | |
| 12 | 2023 | 1 | |
| 13 | 2022 | 12 | |
| 14 | Learning to Deceive Knowledge Graph Augmented Models via Targeted Perturbation | 2021 | 6 |
| 15 | 2019 | 154 | |
| 16 | 2015 | 2 | |
| 17 | 2015 | 12 | |
| 18 | Multilingual Named Entity Recognition using Parallel Data and Metadata from Wikipedia | 2012 | 40 |
| 19 | 2010 | 38 | |
| 20 | Analysis of Thermal Characteristics for the Fire Risk Assessment According to Partial Disconnection on the VCTF and IV Electric Wire | 2008 | 1 |
About Sungchul Kim
Sungchul Kim is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics and Computer Vision and Pattern Recognition, having authored 93 papers that have together received 1.4k indexed citations. Recurring topics across this work include Topic Modeling (17 papers), Advanced Graph Neural Networks (17 papers), Complex Network Analysis Techniques (17 papers), Recommender Systems and Techniques (11 papers), Natural Language Processing Techniques (10 papers), Time Series Analysis and Forecasting (9 papers), Data Visualization and Analytics (8 papers) and Video Analysis and Summarization (7 papers). The work is most often cited by research in Artificial Intelligence (943 citations), Statistical and Nonlinear Physics (360 citations) and Health Informatics (21 citations). Sungchul Kim has collaborated with scholars based in United States, South Korea and China. Frequent co-authors include Ryan A. Rossi, Eunyee Koh, Nesreen K. Ahmed, John Boaz Lee, Giang Nguyen, Hwanjo Yu, Ruiyi Zhang, Anup Rao, Tong Yu and Fan Du. Their work appears in journals such as SHILAP Revista de lepidopterología, BMC Bioinformatics 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.