Yongbin Sun

7.8k citations
44 papers · 4.5k indexed · 1 hit paper · h-index 14

Impact in

Papers in

Yongbin Sun

40 papers receiving 4.4k citations

Hit Papers

Dynamic Graph CNN for Learning on Point Clouds 2019 · 3.9k citations
3.9k201920262021202310002.0k3.0k

Peers

Yongbin Sun
Comparison fields: 5 of 139
  • Geology 1.7k
  • Computer Graphics and Computer-Aided Design 737
  • Computational Mechanics 2.3k
  • Computer Vision and Pattern Recognition 1.7k
  • Environmental Engineering 1.2k
Replace Kaichun Mo with:
Kaichun Mo United States
Qingyong Hu China
Kai Xu China
Zhirong Wu China
Daniel Maturana United States
Andrea Tagliasacchi Canada
Yulan Guo China
Michael Wimmer Austria
Jaesik Park South Korea
Yongbin Sun relative to Kaichun Mo United States Kaichun Mo's profile →
Citations per field
00.5×1.5×2.5×
Kaichun Mo · 1×
Citations per year

Countries citing papers authored by Yongbin Sun

Since Specialization
Citations

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

Fields of papers citing papers by Yongbin Sun

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Yongbin Sun, 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 Yongbin Sun Line = papers co-authored together Yongbin Sun links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20241
2 20247
3 20241
4 202312
5 202312
6 20225
7 202219
8 20216
9 202111
10 202022
11 20205
12 20201
13 2020102
14 20190
15 20197
16
Dynamic Graph CNN for Learning on Point Clouds
Hit paper breakdown →
20193910
17 201831
18 20171
19 20175
20 20100

About Yongbin Sun

Yongbin Sun is a scholar working on Medical Laboratory Technology, Aerospace Engineering, Computer Vision and Pattern Recognition, Computer Graphics and Computer-Aided Design and Statistics, Probability and Uncertainty, having authored 44 papers that have together received 4.5k indexed citations. Recurring topics across this work include Distributed Control Multi-Agent Systems (6 papers), Aerospace Engineering and Control Systems (6 papers), Robotic Path Planning Algorithms (5 papers), Adaptive Control of Nonlinear Systems (4 papers), Nuclear reactor physics and engineering (4 papers), UAV Applications and Optimization (3 papers), Risk and Safety Analysis (3 papers) and 3D Shape Modeling and Analysis (3 papers). The work is most often cited by research in Geology (1.7k citations), Computer Graphics and Computer-Aided Design (737 citations), Computational Mechanics (2.3k citations), Computer Vision and Pattern Recognition (1.7k citations) and Environmental Engineering (1.2k citations). Yongbin Sun has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Sanjay E. Sarma, Ziwei Liu, Yue Wang, Justin Solomon, Michael M. Bronstein, Joshua Siegel, Haibin Duan, Yuhui Shi, Qiang Fu and Xiuyu He. Their work appears in journals such as Aerospace Science and Technology, IEEE Transactions on Aerospace and Electronic Systems, Engineering Applications of Artificial Intelligence, Energies and Electronics.

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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