Binbin Lin

41 papers receiving 453 citations

Peers

Binbin Lin
Comparison fields: 5 of 99
  • Biochemistry 32
  • Computer Vision and Pattern Recognition 96
  • Computer Graphics and Computer-Aided Design 15
  • Geology 24
  • Complementary and alternative medicine 26
Replace Yutian Wu with:
Yutian Wu China
Yueru Chen China
Hanqing Zhang China
Jiajia Zhang China
Weibing Peng China
Meng Xie China
Bingfeng Zhou China
D. Lorente Spain
Wooseok Lee South Korea
Binbin Lin relative to Yutian Wu China Yutian Wu's profile →
Citations per field
00.5×4.2×
Yutian Wu · 1×
Citations per year

Countries citing papers authored by Binbin Lin

Since Specialization
Citations

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

Fields of papers citing papers by Binbin Lin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 47 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201742
2 202337
3 200937
4 202336
5 201323
6 202320
7 199820
8 201119
9 199518
10 201216
11 202116
12 201915
13 201114
14 200214
15 202414
16 201213
17 202311
18 201111
19 201710
20 201310

About Binbin Lin

Binbin Lin is a scholar working on Molecular Biology, Plant Science, Computer Vision and Pattern Recognition, Mechanical Engineering and Materials Chemistry, having authored 47 papers that have together received 466 indexed citations. Recurring topics across this work include Natural product bioactivities and synthesis (6 papers), Advanced Neural Network Applications (6 papers), Genomics and Phylogenetic Studies (4 papers), Phytochemistry and Biological Activities (4 papers), Bioactive natural compounds (4 papers), Video Surveillance and Tracking Methods (3 papers), Microstructure and Mechanical Properties of Steels (3 papers) and Metal Alloys Wear and Properties (3 papers). The work is most often cited by research in Biochemistry (32 citations), Computer Vision and Pattern Recognition (96 citations), Computer Graphics and Computer-Aided Design (15 citations), Geology (24 citations) and Complementary and alternative medicine (26 citations). Binbin Lin has collaborated with scholars based in China, Taiwan and United States. Frequent co-authors include Guo‐Kai Wang, Minjian Qin, Tong He, Xiaofei He, Wanli Ouyang, Honghui Yang, Boxi Wu, Guoyong Xie, Gang Wang and Jinsong Liu. Their work appears in journals such as INTERNATIONAL JOURNAL OF SYSTEMATIC AND EVOLUTIONARY MICROBIOLOGY, IEEE Transactions on Image Processing, Journal of Crystal Growth, Journal of Materials Engineering and Performance and Fitoterapia.

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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