Dongsheng Li

293 papers receiving 4.3k citations

Hit Papers

Correlation Congruence for Knowledge Distillation201720262020202320192022202220172024100200300

Peers

Dongsheng Li
Comparison fields: 5 of 165
  • Artificial Intelligence 1.7k
  • Computer Vision and Pattern Recognition 995
  • Computer Networks and Communications 830
  • Information Systems 744
  • Electrical and Electronic Engineering 416
Replace L.M. Patnaik with:
L.M. Patnaik India
Quan Zhang China
Qi Hao China
Dunwei Gong China
Cesare Alippi Italy
Meiqin Liu China
Minrui Fei China
Suresh Sundaram Singapore
Erik D. Goodman United States
Jianping Wang China
Dongsheng Li relative to L.M. Patnaik India L.M. Patnaik's profile →
Citations per field
00.5×10×15×18×
L.M. Patnaik · 1×
Citations per year

Countries citing papers authored by Dongsheng Li

Since Specialization
Citations

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

Fields of papers citing papers by Dongsheng Li

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dongsheng Li

This figure shows the co-authorship network connecting the top 25 collaborators of Dongsheng Li. A scholar is included among the top collaborators of Dongsheng Li based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Dongsheng Li. Dongsheng Li is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 18
2 3
3 6
4 1
5 3
6 0
7 2
8 4
9 8
10 0
11 34
12 0
13 0
14 16
15 27
16
NENN: Incorporate Node and Edge Features in Graph Neural Networks
12
17 19
18 4
19 0
20
Study on Characteristics of Genetic Algorithms and Its Application to Optimize Reloading Pattern Problem
1

About Dongsheng Li

Dongsheng Li is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computer Networks and Communications, having authored 336 papers that have together received 4.5k indexed citations. Recurring topics across this work include Topic Modeling (45 papers), Caching and Content Delivery (35 papers) and Advanced Neural Network Applications (33 papers). The work is most often cited by research in Artificial Intelligence (1.7k citations), Computer Vision and Pattern Recognition (995 citations) and Computer Networks and Communications (830 citations). Dongsheng Li has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Tao Sun, Xicheng Lu, Linbo Qiao, Yiming Zhang, Bao Wang, Zhaoning Zhang, Zhigang Kan, Yong Dou, Dawei Feng and Ke Yang. Their work appears in journals such as Nature, IEEE Transactions on Pattern Analysis and Machine Intelligence and Scientific Reports.

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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