KiYoung Lee

2.0k citations
23 papers · 1.5k indexed · 1 hit paper · h-index 15
Topics
Machine Learning in Bioinformatics (4 papers)Bioinformatics and Genomic Networks (4 papers)Face and Expression Recognition (3 papers)

In The Last Decade

KiYoung Lee

23 papers receiving 1.4k citations

Hit Papers

Internet traffic classification demystified20082026201420202008100200300

Peers

KiYoung Lee
Comparison fields: 5 of 132
  • Molecular Biology 749
  • Artificial Intelligence 449
  • Genetics 380
  • Computer Networks and Communications 359
  • Cell Biology 202
Replace Atsushi Kanai with:
Atsushi Kanai Japan
Ahmet Saçan United States
Thomas Dean Canada
Matthew Might United States
Yi-Cheng Tu United States
Xiaoning Peng China
L.M. Cheng Hong Kong
Soheil Feizi United States
Dexin Zhang China
Yiming Hu United States
KiYoung Lee relative to Atsushi Kanai Japan Atsushi Kanai's profile →
Citations per field
00.5×4.0×
Atsushi Kanai · 1×
Citations per year

Countries citing papers authored by KiYoung Lee

Since Specialization
Citations

This map shows the geographic impact of KiYoung 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 KiYoung Lee with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites KiYoung Lee more than expected).

Fields of papers citing papers by KiYoung Lee

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by KiYoung 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 KiYoung Lee. The network helps show where KiYoung Lee may publish in the future.

Co-authorship network of co-authors of KiYoung Lee

This figure shows the co-authorship network connecting the top 25 collaborators of KiYoung Lee. A scholar is included among the top collaborators of KiYoung 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 KiYoung Lee. KiYoung Lee is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 11
2 9
3 12
4 32
5 6
6 19
7 12
8 16
9 43
10 67
11 11
12 428
13 23
14 111
15 59
16 1
17
Internet traffic classification demystifiedbreakdown →
367
18 32
19 40
20 7

About KiYoung Lee

KiYoung Lee is a scholar working on Developmental Neuroscience, Neurology and Virology, having authored 23 papers that have together received 1.5k indexed citations. Recurring topics across this work include Machine Learning in Bioinformatics (4 papers), Bioinformatics and Genomic Networks (4 papers) and Face and Expression Recognition (3 papers). The work is most often cited by research in Computer Networks and Communications (359 citations), Artificial Intelligence (449 citations) and Genetics (380 citations). KiYoung Lee has collaborated with scholars based in South Korea, United States and United Kingdom. Frequent co-authors include Doheon Lee, Trey Ideker, T. J. Kim, Dhiman Barman, Marina Fomenkov, Michalis Faloutsos, kc claffy, Kwang H. Lee, Keiichiro Ono and Eigo Suyama. Their work appears in journals such as Nature, Cell and Proceedings of the National Academy of 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.

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