Victor E. Lee
Impact in
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- Complex Network Analysis Techniques
Papers in
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- Advanced Graph Neural Networks 6
- Privacy-Preserving Technologies in Data 2
- Topic Modeling 2
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- Complex Network Analysis Techniques 5
- Co-authors
- Ruoming Jin (8 shared papers)Ning Jun Ruan (2 shared papers)Charų C. Aggarwal (1 shared paper)Feng Zhang (4 shared papers)Hui Hong (1 shared paper)Kim‐Kwang Raymond Choo (2 shared papers)Chunming Rong (1 shared paper)Gansen Zhao (1 shared paper)
- Journals
- Knowledge-Based Systems (1 paper)Journal of Parallel and Distributed Computing (1 paper)Physica A Statistical Mechanics and its Applications (1 paper)ACM Transactions on Knowledge Discovery from Data (1 paper)Information Sciences (1 paper)
- Partner nations
- United StatesChinaAustralia
In The Last Decade
Victor E. Lee
14 papers receiving 522 citations
Peers
Comparison fields: 5 of 64
- Statistical and Nonlinear Physics 162
- Computational Mathematics 6
- Artificial Intelligence 302
- Information Systems 184
- Signal Processing 89
Countries citing papers authored by Victor E. Lee
This map shows the geographic impact of Victor E. 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 Victor E. Lee with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Victor E. Lee more than expected).
Fields of papers citing papers by Victor E. Lee
This network shows the impact of papers produced by Victor E. 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 Victor E. Lee. The network helps show where Victor E. Lee may publish in the future.
Co-authors
The 25 scholars most cited alongside Victor E. 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
| # | Work | ||
|---|---|---|---|
| 1 | 2010 | 165 | |
| 2 | 2016 | 80 | |
| 3 | 2011 | 72 | |
| 4 | 2009 | 43 | |
| 5 | 2018 | 41 | |
| 6 | 2021 | 35 | |
| 7 | 2018 | 28 | |
| 8 | 2014 | 26 | |
| 9 | 2014 | 24 | |
| 10 | 2018 | 6 | |
| 11 | Decision Trees: Theory and Algorithms. | 2014 | 6 |
| 12 | 2009 | 3 | |
| 13 | 2015 | 3 | |
| 14 | 2022 | 2 |
About Victor E. Lee
Victor E. Lee is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics, Management Science and Operations Research, Information Systems and Computer Vision and Pattern Recognition, having authored 14 papers that have together received 534 indexed citations. Recurring topics across this work include Advanced Graph Neural Networks (6 papers), Complex Network Analysis Techniques (5 papers), Data Mining Algorithms and Applications (3 papers), Bioinformatics and Genomic Networks (3 papers), Privacy-Preserving Technologies in Data (2 papers), Topic Modeling (2 papers), Data Management and Algorithms (2 papers) and Graph Theory and Algorithms (2 papers). The work is most often cited by research in Statistical and Nonlinear Physics (162 citations), Computational Mathematics (6 citations), Artificial Intelligence (302 citations), Information Systems (184 citations) and Signal Processing (89 citations). Victor E. Lee has collaborated with scholars based in United States, China and Australia. Frequent co-authors include Ruoming Jin, Ning Jun Ruan, Charų C. Aggarwal, Feng Zhang, Hui Hong, Kim‐Kwang Raymond Choo, Chunming Rong, Gansen Zhao, Guangzhi Qu and Longjie Li. Their work appears in journals such as Knowledge-Based Systems, Journal of Parallel and Distributed Computing, Physica A Statistical Mechanics and its Applications, ACM Transactions on Knowledge Discovery from Data 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.