Junyan Tan

807 citations
56 papers · 620 · h-index 15

Impact in

Papers in

Junyan Tan

55 papers receiving 603 citations

Peers

Junyan Tan
Comparison fields: 5 of 101
  • Biomaterials 95
  • Organic Chemistry 147
  • Computational Mathematics 3
  • Computer Vision and Pattern Recognition 103
  • Materials Chemistry 200
Replace Guowei Gao with:
Guowei Gao China
Ning Cao China
Jianglong Liu China
Haili Zhang China
Hazri Bakhtiar Malaysia
Zhijing Liu China
Jun Qiu China
Huixing Zhang China
Zhonglin Cao United States
Junyan Tan relative to Guowei Gao China Guowei Gao's profile →
Citations per field
00.5×1.5×
Guowei Gao · 1×
Citations per year

Countries citing papers authored by Junyan Tan

Since Specialization
Citations

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

Fields of papers citing papers by Junyan Tan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 56 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201964
2 201947
3 201934
4 202334
5 202134
6 201928
7 201227
8 201724
9 201823
10 201918
11 201817
12 201817
13 201217
14 202016
15 201914
16 201813
17 201313
18 201413
19 202213
20 201813

About Junyan Tan

Junyan Tan is a scholar working on Computer Vision and Pattern Recognition, Materials Chemistry, Artificial Intelligence, Electrical and Electronic Engineering and Organic Chemistry, having authored 56 papers that have together received 620 indexed citations. Recurring topics across this work include Face and Expression Recognition (15 papers), Polyoxometalates: Synthesis and Applications (8 papers), Interconnection Networks and Systems (6 papers), Machine Learning and ELM (5 papers), Remote-Sensing Image Classification (5 papers), Embedded Systems Design Techniques (5 papers), Supramolecular Self-Assembly in Materials (4 papers) and Metal-Organic Frameworks: Synthesis and Applications (4 papers). The work is most often cited by research in Biomaterials (95 citations), Organic Chemistry (147 citations), Computational Mathematics (3 citations), Computer Vision and Pattern Recognition (103 citations) and Materials Chemistry (200 citations). Junyan Tan has collaborated with scholars based in China, France and Iran. Frequent co-authors include Jie Zhang, Xinhua Wan, Yue Zhou, Nai-Yang Deng, Zhiqiang Zhang, Zhen Ling, Yan Guan, Kun Chen, Mengyan Zeng and Nan Shi. Their work appears in journals such as Chemical Communications, Neural Computing and Applications, Neurocomputing, Engineering Applications of Artificial Intelligence and Applied Intelligence.

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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