Shikun Feng

3.2k citations
37 papers · 1.2k indexed · 2 hit papers · h-index 13

Shikun Feng

32 papers receiving 1.2k citations

Hit Papers

Masked Label Prediction: Unified Message Passing Model fo...3372020202620222024100200300400

Peers

Shikun Feng
Comparison fields: 5 of 132
  • Artificial Intelligence 670
  • Computer Vision and Pattern Recognition 392
  • Health Informatics 9
  • Signal Processing 70
  • Computer Graphics and Computer-Aided Design 21
Replace Minsuk Kahng with:
Minsuk Kahng United States
Erik Nijkamp United States
Yuexin Wu China
Yuxin Ma China
Ilir Jusufi Sweden
Tie‐Yan Liu China
Hossein Ebrahimpour-Komleh Iran
Bowen Liu China
Chenglong Wang China
Shikun Feng relative to Minsuk Kahng United States Minsuk Kahng's profile →
Citations per field
00.5×1.5×2.5×
Minsuk Kahng · 1×
Citations per year

Countries citing papers authored by Shikun Feng

Since Specialization
Citations

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

Fields of papers citing papers by Shikun Feng

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

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

All Works

20 of 20 papers shown
#Work
1 20251
2 20250
3 20250
4 20243
5 20240
6 20243
7 202413
8 20248
9 20244
10 20240
11 20241
12 202325
13 20234
14 202233
15 202213
16 20208
17 201913
18 201421
19 201246
20 20092

About Shikun Feng

Shikun Feng is a scholar working on Aquatic Science, Computer Vision and Pattern Recognition and Computer Graphics and Computer-Aided Design, having authored 37 papers that have together received 1.2k indexed citations. Recurring topics across this work include Topic Modeling (9 papers), Aquaculture disease management and microbiota (8 papers), Multimodal Machine Learning Applications (7 papers), Aquaculture Nutrition and Growth (6 papers), Advanced Image and Video Retrieval Techniques (6 papers), Natural Language Processing Techniques (6 papers), Advanced Graph Neural Networks (5 papers) and Video Analysis and Summarization (3 papers). The work is most often cited by research in Artificial Intelligence (670 citations), Computer Vision and Pattern Recognition (392 citations) and Health Informatics (9 citations). Shikun Feng has collaborated with scholars based in China, Singapore and Egypt. Frequent co-authors include Yu Sun, Haifeng Wang, Zhengjie Huang, Shuohuan Wang, Hao Tian, Hua Wu, Yukun Li, Yunsheng Shi, Hui Zhong and Wenjing Wang. Their work appears in journals such as Aquaculture Reports, Ceramics International, Journal of the Science of Food and Agriculture, Aquaculture Nutrition and Nature Machine 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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