U Kang

151 papers receiving 3.7k citations

Hit Papers

PEGASUS: A Peta-Scale Graph Mining System Implementation and Observations 2009 · 426 citations
4260+5+11Years since publication100200300400

Peers

U Kang
Comparison fields: 5 of 143
  • Computational Mathematics 523
  • Statistical and Nonlinear Physics 1.0k
  • Computer Vision and Pattern Recognition 1.5k
  • Artificial Intelligence 1.9k
  • Hardware and Architecture 337
Replace James Cheng with:
James Cheng Hong Kong
Evangelos E. Papalexakis United States
Hong Cheng Hong Kong
Quanquan Gu United States
Spiros Papadimitriou United States
Christopher Ré United States
Danai Koutra United States
Domonkos Tikk Hungary
Steve Uhlig United Kingdom
Hisashi Kashima Japan
U Kang relative to James Cheng Hong Kong James Cheng's profile →
Citations per field
00.5×1.5×2.3×
James Cheng · 1×
Citations per year

Countries citing papers authored by U Kang

Since Specialization
Citations

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

Fields of papers citing papers by U Kang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside U Kang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with U Kang Line = papers co-authored together U Kang links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 161 papers — load more, or switch the sort, to bring in the rest.

#Work
1
PEGASUS: A Peta-Scale Graph Mining System Implementation and Observations
Hit paper breakdown →
2009426
2 2009185
3 2000184
4 2012184
5 2011106
6 201095
7 201495
8 201592
9 201187
10 201183
11 201179
12 201479
13 201879
14 202178
15 201170
16 201264
17 201562
18 201554
19 201651
20 202148

About U Kang

U Kang is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics, Information Systems and Computational Mathematics, having authored 161 papers that have together received 3.8k indexed citations. Recurring topics across this work include Graph Theory and Algorithms (47 papers), Advanced Graph Neural Networks (44 papers), Complex Network Analysis Techniques (44 papers), Tensor decomposition and applications (25 papers), Data Management and Algorithms (13 papers), Parallel Computing and Optimization Techniques (12 papers), Topic Modeling (10 papers) and Domain Adaptation and Few-Shot Learning (10 papers). The work is most often cited by research in Computational Mathematics (523 citations), Statistical and Nonlinear Physics (1.0k citations), Computer Vision and Pattern Recognition (1.5k citations), Artificial Intelligence (1.9k citations) and Hardware and Architecture (337 citations). U Kang has collaborated with scholars based in South Korea, United States and Italy. Frequent co-authors include Christos Faloutsos, Charalampos E. Tsourakakis, K.D. Wise, Yongsub Lim, Evangelos E. Papalexakis, Lee Sael, Jinhong Jung, Hanghang Tong, Jimeng Sun and Jaemin Yoo. Their work appears in journals such as PLoS ONE, Knowledge and Information Systems, ACM Transactions on Knowledge Discovery from Data, The VLDB Journal and IEEE Transactions on Knowledge and Data Engineering.

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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