Sheng Guan

68 total papers · 687 total citations
38 papers, 512 citations indexed

About

Sheng Guan is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing. According to data from OpenAlex, Sheng Guan has authored 38 papers receiving a total of 512 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Artificial Intelligence, 7 papers in Computer Vision and Pattern Recognition and 6 papers in Signal Processing. Recurrent topics in Sheng Guan's work include Advanced Graph Neural Networks (9 papers), Anomaly Detection Techniques and Applications (4 papers) and Speech and Audio Processing (3 papers). Sheng Guan is often cited by papers focused on Advanced Graph Neural Networks (9 papers), Anomaly Detection Techniques and Applications (4 papers) and Speech and Audio Processing (3 papers). Sheng Guan collaborates with scholars based in China, United States and Singapore. Sheng Guan's co-authors include Xiangmin Zhang, Mingxia Gao, Guoquan Yan, Hailong Yu, Haoyang Zheng, Jiandong Zhao, Xi Shao, Chunhui Deng, Yinghui Wu and Ka Lok Man and has published in prestigious journals such as Analytical Chemistry, IEEE Access and Analytical and Bioanalytical Chemistry.

In The Last Decade

Sheng Guan

36 papers receiving 501 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Sheng Guan 286 113 82 82 80 38 512
Yida Huang 215 0.8× 29 0.3× 74 0.9× 79 1.0× 90 1.1× 30 450
Shaokun Wang 74 0.3× 53 0.5× 71 0.9× 18 0.2× 57 0.7× 68 607
Andrea Ridolfi 276 1.0× 71 0.6× 65 0.8× 9 0.1× 128 1.6× 43 575
Jun Ren 148 0.5× 18 0.2× 135 1.6× 15 0.2× 47 0.6× 29 535
Sangho Lee 243 0.8× 53 0.5× 49 0.6× 11 0.1× 79 1.0× 29 538
King Wai Lau 282 1.0× 74 0.7× 111 1.4× 154 1.9× 25 0.3× 19 581
Lingzhi Hu 195 0.7× 104 0.9× 101 1.2× 20 0.2× 121 1.5× 28 614
Kaoru Nakano 71 0.2× 25 0.2× 231 2.8× 13 0.2× 78 1.0× 24 550
Qi Gao 139 0.5× 72 0.6× 23 0.3× 9 0.1× 36 0.5× 56 601
Yifeng Tao 223 0.8× 25 0.2× 76 0.9× 27 0.3× 71 0.9× 31 457

Countries citing papers authored by Sheng Guan

Since Specialization
Citations

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

Fields of papers citing papers by Sheng Guan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sheng Guan

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

All Works

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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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