Song De

1.5k citations
61 papers · 1.2k · h-index 17

Impact in

Papers in

Song De

56 papers receiving 1.1k citations

Peers

Song De
Comparison fields: 5 of 78
  • Computer Graphics and Computer-Aided Design 85
  • Computer Vision and Pattern Recognition 439
  • Polymers and Plastics 196
  • Geology 68
  • Media Technology 102
Replace Satoshi Ikehata with:
Satoshi Ikehata Japan
A. Grunnet-Jepsen United States
Youri Meuret Belgium
Jonathan Adams Canada
Qun Hao China
T. Honda Japan
A.G. Jordan United States
Kota Ito Japan
Elena Stoykova Bulgaria
C. Brosseau France
Song De relative to Satoshi Ikehata Japan Satoshi Ikehata's profile →
Citations per field
00.5×4.4×
Satoshi Ikehata · 1×
Citations per year

Countries citing papers authored by Song De

Since Specialization
Citations

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

Fields of papers citing papers by Song De

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 61 papers — load more, or switch the sort, to bring in the rest.

#Work
1 1994218
2 2007108
3 199385
4 200881
5 200966
6 198553
7 199448
8 200839
9 198838
10 199236
11 200934
12 200831
13 200928
14 199524
15 198624
16 199822
17 199319
18 200716
19 200714
20 199712

About Song De

Song De is a scholar working on Electrical and Electronic Engineering, Computer Vision and Pattern Recognition, Atomic and Molecular Physics, and Optics, Computational Mechanics and Computer Graphics and Computer-Aided Design, having authored 61 papers that have together received 1.2k indexed citations. Recurring topics across this work include Organic Electronics and Photovoltaics (13 papers), Advanced Vision and Imaging (11 papers), Semiconductor materials and devices (8 papers), Image and Object Detection Techniques (8 papers), Molecular Junctions and Nanostructures (7 papers), Quantum and electron transport phenomena (7 papers), Conducting polymers and applications (6 papers) and Computer Graphics and Visualization Techniques (6 papers). The work is most often cited by research in Computer Graphics and Computer-Aided Design (85 citations), Computer Vision and Pattern Recognition (439 citations), Polymers and Plastics (196 citations), Geology (68 citations) and Media Technology (102 citations). Song De has collaborated with scholars based in China, United States and France. Frequent co-authors include Guo-Qing Wei, Donghang Yan, Yanhou Geng, André Gagalowicz, Haibo Wang, F. C. Wellstood, Feng Zhu, Junliang Yang, Hongkun Tian and C. J. Lobb. Their work appears in journals such as Applied Physics Letters, IEEE Transactions on Applied Superconductivity, Pattern Recognition, Pattern Recognition Letters and Computers & Graphics.

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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