Kunhong Liu

2.1k total citations
101 papers, 1.5k citations indexed

About

Kunhong Liu is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Molecular Biology. According to data from OpenAlex, Kunhong Liu has authored 101 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 40 papers in Artificial Intelligence, 27 papers in Computer Vision and Pattern Recognition and 26 papers in Molecular Biology. Recurrent topics in Kunhong Liu's work include Gene expression and cancer classification (16 papers), Face and Expression Recognition (14 papers) and Emotion and Mood Recognition (14 papers). Kunhong Liu is often cited by papers focused on Gene expression and cancer classification (16 papers), Face and Expression Recognition (14 papers) and Emotion and Mood Recognition (14 papers). Kunhong Liu collaborates with scholars based in China, United Kingdom and Taiwan. Kunhong Liu's co-authors include De-Shuang Huang, Jisheng Zhou, Guanjun Zhang, Qingqiang Wu, Huaihe Song, Sze‐Teng Liong, Chao Wang, Bo Li, Qingqi Hong and Beizhan Wang and has published in prestigious journals such as SHILAP Revista de lepidopterología, Bioinformatics and Journal of Materials Chemistry A.

In The Last Decade

Kunhong Liu

95 papers receiving 1.4k citations

Peers

Kunhong Liu
Comparison fields: 5 of 121
  • Artificial Intelligence 474
  • Computer Vision and Pattern Recognition 397
  • Electrical and Electronic Engineering 322
  • Molecular Biology 223
  • Signal Processing 153
Replace Zhongyuan Zhang with:
Zhongyuan Zhang China
Quan Liu China
Jianpei Zhang China
Xinjing Wang China
Xin Liu China
Xia Yin China
Bo Yuan United States
Yun Tan China
Jinkyu Kim South Korea
Zhongyuan Zhang China View profile →
Citations per field, relative to Kunhong Liu
Kunhong Liu · 1×
Citations per year, relative to Kunhong Liu
Kunhong Liu · 1×

Countries citing papers authored by Kunhong Liu

Since Specialization
Citations

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

Fields of papers citing papers by Kunhong Liu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kunhong Liu

This figure shows the co-authorship network connecting the top 25 collaborators of Kunhong Liu. A scholar is included among the top collaborators of Kunhong Liu 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 Kunhong Liu. Kunhong Liu 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 0
3 1
4 1
5 0
6 0
7 1
8 2
9 1
10 6
11 3
12 23
13 4
14 1
15 9
16 9
17 3
18 9
19 3
20 2

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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