Ran Xin

1.5k citations
36 papers · 942 · h-index 12

Impact in

Papers in

Ran Xin

34 papers receiving 922 citations

Peers

Ran Xin
Comparison fields: 5 of 73
  • Computer Networks and Communications 595
  • Computational Mechanics 248
  • Artificial Intelligence 386
  • Safety Research 81
  • Numerical Analysis 21
Replace Ingo Althöfer with:
Ingo Althöfer Germany
S. Sundhar Ram United States
Hamed Hassani United States
Vinod M. Prabhakaran India
Vyacheslav Kungurtsev Czechia
Thorsten Theobald Germany
Huy Lê Nguyễn United States
Amit Agarwal India
Po‐Ning Chen Taiwan
Sisira K. Weeratunga United States
Ran Xin relative to Ingo Althöfer Germany Ingo Althöfer's profile →
Citations per field
00.5×5×10×16.2×
Ingo Althöfer · 1×
Citations per year

Countries citing papers authored by Ran Xin

Since Specialization
Citations

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

Fields of papers citing papers by Ran Xin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 36 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2018149
2 2019130
3 202086
4 202079
5 202076
6 201970
7 201762
8 202162
9 202062
10 202031
11 202230
12 202129
13 201012
14 201210
15 20128
16 20208
17 20166
18 20106
19 20233
20 20153

About Ran Xin

Ran Xin is a scholar working on Computer Networks and Communications, Computational Mechanics, Artificial Intelligence, Computer Vision and Pattern Recognition and Aerospace Engineering, having authored 36 papers that have together received 942 indexed citations. Recurring topics across this work include Distributed Control Multi-Agent Systems (15 papers), Stochastic Gradient Optimization Techniques (13 papers), Sparse and Compressive Sensing Techniques (12 papers), Infrared Target Detection Methodologies (8 papers), Visual Attention and Saliency Detection (7 papers), Neural Networks Stability and Synchronization (3 papers), Image Enhancement Techniques (3 papers) and Olfactory and Sensory Function Studies (3 papers). The work is most often cited by research in Computer Networks and Communications (595 citations), Computational Mechanics (248 citations), Artificial Intelligence (386 citations), Safety Research (81 citations) and Numerical Analysis (21 citations). Ran Xin has collaborated with scholars based in China, United States and France. Frequent co-authors include Usman A. Khan, Soummya Kar, Eyad H. Abed, Chenguang Xi, Van Sy Mai, Shi Pu, Angelia Nedich, Anit Kumar Sahu, Xiaodong Jin and Shifan Wang. Their work appears in journals such as IEEE Transactions on Automatic Control, SIAM Journal on Optimization, IEEE Control Systems Letters, IEEE Transactions on Signal Processing and Sensors and Actuators B Chemical.

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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