Luping Liu

749 total citations
40 papers, 444 citations indexed

About

Luping Liu is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computer Networks and Communications. According to data from OpenAlex, Luping Liu has authored 40 papers receiving a total of 444 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Artificial Intelligence, 12 papers in Computer Vision and Pattern Recognition and 10 papers in Computer Networks and Communications. Recurrent topics in Luping Liu's work include Topic Modeling (9 papers), Network Security and Intrusion Detection (7 papers) and Natural Language Processing Techniques (7 papers). Luping Liu is often cited by papers focused on Topic Modeling (9 papers), Network Security and Intrusion Detection (7 papers) and Natural Language Processing Techniques (7 papers). Luping Liu collaborates with scholars based in China, United States and United Kingdom. Luping Liu's co-authors include Linbo Qing, Peng Jia, Honggang Chen, Zhiying Wang, Fang Liu, Zhiping Cai, Nong Xiao, Cheng Huang, Meiling Wang and Yan Wang and has published in prestigious journals such as PLoS ONE, Optics Express and IEEE Access.

In The Last Decade

Luping Liu

35 papers receiving 425 citations

Peers

Luping Liu
Comparison fields: 5 of 70
  • Artificial Intelligence 163
  • Computer Networks and Communications 134
  • Computer Vision and Pattern Recognition 106
  • Information Systems 92
  • Signal Processing 80
Replace Wan Azizun Wan Adnan with:
Wan Azizun Wan Adnan Malaysia
Vandana Bhattacharjee India
Mohamed Abu ElSoud Egypt
Rita Chhikara India
Sapiah Sakri Saudi Arabia
Iker Gondra Canada
Amr T. Abdel-Hamid Egypt
Yu Wen China
Tianxi Ji United States
Wan Azizun Wan Adnan Malaysia View profile →
Citations per field, relative to Luping Liu
Luping Liu · 1×
Citations per year, relative to Luping Liu
Luping Liu · 1×

Countries citing papers authored by Luping Liu

Since Specialization
Citations

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

Fields of papers citing papers by Luping Liu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Luping Liu

This figure shows the co-authorship network connecting the top 25 collaborators of Luping Liu. A scholar is included among the top collaborators of Luping Liu 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 Luping Liu. Luping Liu 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
# Work Indexed citations
1 0
2 0
3 0
4 3
5 3
6 3
7 27
8 2
9 47
10 17
11 7
12 3
13 23
14 47
15 8
16 23
17 4
18 16
19 2
20 5

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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