Kan Li

1.7k citations
99 papers · 1.0k · h-index 19

Impact in

Papers in

Kan Li

90 papers receiving 981 citations

Peers

Kan Li
Comparison fields: 5 of 105
  • Statistical and Nonlinear Physics 250
  • Computer Science Applications 97
  • Artificial Intelligence 434
  • Information Systems 270
  • Computer Vision and Pattern Recognition 200
Replace Erheng Zhong with:
Erheng Zhong Hong Kong
Jilei Tian China
Bert Huang United States
Yanhao Wang China
Liang Gou United States
Jiuxin Cao China
Cheng–Te Li Taiwan
Gita Sukthankar United States
Tianyi Wang China
Pei Li China
Kan Li relative to Erheng Zhong Hong Kong Erheng Zhong's profile →
Citations per field
00.5×1.5×2.1×
Erheng Zhong · 1×
Citations per year

Countries citing papers authored by Kan Li

Since Specialization
Citations

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

Fields of papers citing papers by Kan Li

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 99 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2019109
2 201994
3 201854
4 201851
5 201837
6 201935
7 201932
8 202030
9 201925
10 201725
11 200924
12 201624
13 201824
14 201923
15 202022
16 201522
17 202021
18 202119
19 201918
20 201917

About Kan Li

Kan Li is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems, Statistical and Nonlinear Physics and Computer Networks and Communications, having authored 99 papers that have together received 1.0k indexed citations. Recurring topics across this work include Topic Modeling (20 papers), Complex Network Analysis Techniques (16 papers), Speech and dialogue systems (14 papers), Opinion Dynamics and Social Influence (14 papers), Human Pose and Action Recognition (10 papers), Multimodal Machine Learning Applications (9 papers), Recommender Systems and Techniques (9 papers) and Natural Language Processing Techniques (8 papers). The work is most often cited by research in Statistical and Nonlinear Physics (250 citations), Computer Science Applications (97 citations), Artificial Intelligence (434 citations), Information Systems (270 citations) and Computer Vision and Pattern Recognition (200 citations). Kan Li has collaborated with scholars based in China, Australia and Pakistan. Frequent co-authors include Sadique Ahmad, Guangquan Zhang, Jie Lü, Yushu Liu, Heyan Huang, Lin Zhang, Shuhui Zhang, Yongchao Wang, Arshad Ahmad and Fengjiao Chen. Their work appears in journals such as Knowledge-Based Systems, Neurocomputing, IEEE Access, Applied Intelligence and Applied Sciences.

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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