Yu Guan

522 total citations
22 papers, 387 citations indexed

About

Yu Guan is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Computational Mathematics. According to data from OpenAlex, Yu Guan has authored 22 papers receiving a total of 387 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Artificial Intelligence, 6 papers in Computational Theory and Mathematics and 4 papers in Computational Mathematics. Recurrent topics in Yu Guan's work include Tensor decomposition and applications (4 papers), Sparse and Compressive Sensing Techniques (3 papers) and Remote-Sensing Image Classification (3 papers). Yu Guan is often cited by papers focused on Tensor decomposition and applications (4 papers), Sparse and Compressive Sensing Techniques (3 papers) and Remote-Sensing Image Classification (3 papers). Yu Guan collaborates with scholars based in China, Canada and Belgium. Yu Guan's co-authors include Delin Chu, Tieli Sun, Moody T. Chu, Guangyong Chen, Min Gan, C. L. Philip Chen, R.I. Carr, Jaroslav A. Kralovec, Yang-Geng Fu and H. Stephen Ewart and has published in prestigious journals such as IEEE Access, IEEE Transactions on Neural Networks and Learning Systems and Knowledge-Based Systems.

In The Last Decade

Yu Guan

22 papers receiving 373 citations

Peers

Yu Guan
Comparison fields: 5 of 113
  • Artificial Intelligence 81
  • Computational Theory and Mathematics 64
  • Computational Mathematics 59
  • Control and Systems Engineering 53
  • Computer Vision and Pattern Recognition 39
Replace Hongli Lv with:
Hongli Lv China
Shan Zeng China
Dheevatsa Mudigere United States
Yu Pan China
Tian Zhao United States
Chongke Bi China
Zhou Jin China
Andrej Gisbrecht Germany
Jinrong Cui China
Dingsheng Wan China
Hongli Lv China View profile →
Citations per field, relative to Yu Guan
Yu Guan · 1×
Citations per year, relative to Yu Guan
Yu Guan · 1×

Countries citing papers authored by Yu Guan

Since Specialization
Citations

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

Fields of papers citing papers by Yu Guan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yu Guan

This figure shows the co-authorship network connecting the top 25 collaborators of Yu Guan. A scholar is included among the top collaborators of Yu Guan 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 Yu Guan. Yu Guan 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
# Work Indexed citations
1 2
2 25
3 15
4 57
5 20
6 22
7 9
8 17
9 26
10 1
11 70
12 7
13 2
14
Analysis of information flow control in military supply chain management
5
15 5
16 5
17 4
18 1
19 56
20 9

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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