Weiping Song

697 citations
3 papers · 31 indexed · h-index 2
Topics
Advanced Graph Neural Networks (1 paper)Engineering Applied Research (1 paper)Advanced Aircraft Design and Technologies (1 paper)
Journals
International Journal of Automotive TechnologyarXiv (Cornell University)Journal of Southwest China Normal University
Partner nations
ChinaCanada

In The Last Decade

Weiping Song

3 papers receiving 31 citations

Peers

Weiping Song
Comparison fields: 5 of 19
  • Artificial Intelligence 26
  • Information Systems 7
  • Computer Vision and Pattern Recognition 4
  • Computational Theory and Mathematics 4
  • Molecular Biology 3
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Hart Montgomery United States
Silas Richelson United States
Po-Wei Wang United States
Fernando Virdia United Kingdom
Ward Beullens Switzerland
Pratish Datta India
Tamara von Glehn United Kingdom
Elena Kirshanova Russia
Huaxiong Wang Singapore
Daniel Smith-Tone United States
Weiping Song relative to Hart Montgomery United States Hart Montgomery's profile →
Citations per field
00.5×10×
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Citations per year

Countries citing papers authored by Weiping Song

Since Specialization
Citations

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

Fields of papers citing papers by Weiping Song

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Weiping Song

This figure shows the co-authorship network connecting the top 25 collaborators of Weiping Song. A scholar is included among the top collaborators of Weiping Song 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 Weiping Song. Weiping Song is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

3 of 3 papers shown
#WorkIndexed citations
1 29
2 1
3
Self-organized Competing Neural Network and Its Application in Compartmentalizing Social Economy Areas
1

About Weiping Song

Weiping Song is a scholar working on Automotive Engineering, Global and Planetary Change and Civil and Structural Engineering, having authored 3 papers that have together received 31 indexed citations. Recurring topics across this work include Advanced Graph Neural Networks (1 paper), Engineering Applied Research (1 paper) and Advanced Aircraft Design and Technologies (1 paper). The work is most often cited by research in Artificial Intelligence (26 citations), Information Systems (7 citations) and Computational Theory and Mathematics (4 citations). Weiping Song has collaborated with scholars based in China and Canada. Frequent co-authors include Meng Qu, Wei Ju, Ming Zhang, Jianhao Shen, Yang Liu and Yaohua Li. Their work appears in journals such as International Journal of Automotive Technology, arXiv (Cornell University) and Journal of Southwest China Normal University.

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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