Ivan Lee
Impact in
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- scientometrics and bibliometrics research
- Polymers and Plastics top 5%
- Conducting polymers and applications
Papers in
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- Advanced Data Compression Techniques 11
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- Advanced Graph Neural Networks 12
- Co-authors
- Xiaomei Bai (12 shared papers)Feng Xia (17 shared papers)Natalia Abuladze (7 shared papers)Debra K. Newman (6 shared papers)Alexander Pushkin (6 shared papers)Ira Kurtz (6 shared papers)Jun Ma (5 shared papers)Xiangjie Kong (10 shared papers)
In The Last Decade
Ivan Lee
212 papers receiving 4.8k citations
Ivan Lee's Hit Papers
Peers
Comparison fields: 5 of 197
- Statistics, Probability and Uncertainty 266
- Polymers and Plastics 348
- Artificial Intelligence 831
- Computer Networks and Communications 573
- Computer Vision and Pattern Recognition 455
Countries citing papers authored by Ivan Lee
This map shows the geographic impact of Ivan 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 Ivan Lee with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ivan Lee more than expected).
Fields of papers citing papers by Ivan Lee
This network shows the impact of papers produced by Ivan 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 Ivan Lee. The network helps show where Ivan Lee may publish in the future.
Co-authors
The 25 scholars most cited alongside Ivan Lee, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 224 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Highly Sensitive, Wearable, Durable Strain Sensors and Stretchable Conductors Using Graphene/Silicon Rubber Composites Hit paper breakdown → | 2016 | 364 |
| 2 | 2007 | 242 | |
| 3 | 1998 | 233 | |
| 4 | 2019 | 208 | |
| 5 | 2018 | 188 | |
| 6 | 1999 | 176 | |
| 7 | 2019 | 137 | |
| 8 | 2012 | 130 | |
| 9 | 2007 | 114 | |
| 10 | 2016 | 105 | |
| 11 | 2019 | 98 | |
| 12 | 2004 | 96 | |
| 13 | 2000 | 89 | |
| 14 | 2016 | 87 | |
| 15 | 2000 | 84 | |
| 16 | 2004 | 76 | |
| 17 | 2018 | 76 | |
| 18 | 2016 | 71 | |
| 19 | 2021 | 54 | |
| 20 | 2009 | 53 |
About Ivan Lee
Ivan Lee is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Computer Networks and Communications, Signal Processing and Biomedical Engineering, having authored 224 papers that have together received 5.0k indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (18 papers), Video Coding and Compression Technologies (17 papers), scientometrics and bibliometrics research (13 papers), Spectroscopy and Chemometric Analyses (12 papers), Advanced Graph Neural Networks (12 papers), Caching and Content Delivery (12 papers), Advanced Data Compression Techniques (11 papers) and Peer-to-Peer Network Technologies (11 papers). The work is most often cited by research in Statistics, Probability and Uncertainty (266 citations), Polymers and Plastics (348 citations), Artificial Intelligence (831 citations), Computer Networks and Communications (573 citations) and Computer Vision and Pattern Recognition (455 citations). Ivan Lee has collaborated with scholars based in Australia, China and Canada. Frequent co-authors include Xiaomei Bai, Feng Xia, Natalia Abuladze, Debra K. Newman, Alexander Pushkin, Ira Kurtz, Jun Ma, Xiangjie Kong, Ling Guan and Zhaolong Ning. Their work appears in journals such as IEEE Access, IEEE Transactions on Industrial Informatics, PLoS ONE, Scientometrics and IEEE Transactions on Multimedia.
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.