Jun Yan

2.9k citations
95 papers · 1.9k · h-index 27

Impact in

Papers in

Jun Yan

88 papers receiving 1.8k citations

Peers

Jun Yan
Comparison fields: 5 of 150
  • Artificial Intelligence 1.0k
  • Computational Mathematics 17
  • Health Informatics 32
  • Computer Vision and Pattern Recognition 440
  • Information Systems 376
Replace Tong Xu with:
Tong Xu China
Fabio Aiolli Italy
Troy Raeder United States
Sungchul Kim United States
Sen Wu China
Fang Han United States
Max Chickering United States
Zeno Gantner Germany
Tianyong Hao China
Jun Yan relative to Tong Xu China Tong Xu's profile →
Citations per field
00.5×10×15×17.8×
Tong Xu · 1×
Citations per year

Countries citing papers authored by Jun Yan

Since Specialization
Citations

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

Fields of papers citing papers by Jun Yan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 95 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2020188
2 2006148
3 2007112
4 201994
5 201885
6 200579
7 200868
8 200865
9 201053
10 200648
11 202047
12 200446
13 201845
14 202045
15 200935
16 201535
17 202133
18 201733
19 201030
20 200929

About Jun Yan

Jun Yan is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems, Sociology and Political Science and Marketing, having authored 95 papers that have together received 1.9k indexed citations. Recurring topics across this work include Text and Document Classification Technologies (25 papers), Topic Modeling (22 papers), Face and Expression Recognition (13 papers), Natural Language Processing Techniques (13 papers), Web Data Mining and Analysis (11 papers), Image Retrieval and Classification Techniques (10 papers), Advanced Image and Video Retrieval Techniques (8 papers) and Digital Marketing and Social Media (8 papers). The work is most often cited by research in Artificial Intelligence (1.0k citations), Computational Mathematics (17 citations), Health Informatics (32 citations), Computer Vision and Pattern Recognition (440 citations) and Information Systems (376 citations). Jun Yan has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Benyu Zhang, Shuicheng Yan, Tianyong Hao, Qiang Yang, Xieling Chen, Weiguo Fan, Buzhou Tang, Qiansheng Cheng, Zheng Chen and Zheng Chen. Their work appears in journals such as BMC Medical Informatics and Decision Making, IEEE Transactions on Knowledge and Data Engineering, International Journal of Security and Networks, Journal of Medical Internet Research and Information Retrieval.

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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