Pu Li

500 citations
43 papers · 322 · h-index 9

Impact in

    • Topic Modeling
    • Sentiment Analysis and Opinion Mining
    • Advanced Graph Neural Networks
    • Advanced Text Analysis Techniques
    • Semantic Web and Ontologies
    • Recommender Systems and Techniques
    • Information Retrieval and Search Behavior

Papers in

Pu Li

35 papers receiving 310 citations

Peers

Pu Li
Comparison fields: 5 of 92
  • Artificial Intelligence 167
  • Information Systems 98
  • Computer Vision and Pattern Recognition 57
  • Computer Science Applications 12
  • Media Technology 17
Replace Khuyagbaatar Batsuren with:
Khuyagbaatar Batsuren Italy
Anak Agung Putri Ratna Indonesia
Sashikala Mishra India
Dongyeop Kang United States
Muhammed J. A. Patwary Bangladesh
Mohammad Reza Kangavari Iran
Xinguo Yu China
R. Raja Subramanian India
Yixin Zhong China
Pu Li relative to Khuyagbaatar Batsuren Italy Khuyagbaatar Batsuren's profile →
Citations per field
00.5×2.6×
Khuyagbaatar Batsuren · 1×
Citations per year

Countries citing papers authored by Pu Li

Since Specialization
Citations

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

Fields of papers citing papers by Pu Li

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 43 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202296
2 200736
3 202330
4 201715
5 202214
6 202212
7 202211
8 20209
9 20149
10 20138
11 20177
12 20227
13 20207
14 20206
15 20236
16 20196
17 20175
18 20234
19 20193
20 20203

About Pu Li

Pu Li is a scholar working on Artificial Intelligence, Information Systems, Computer Vision and Pattern Recognition, Computer Networks and Communications and Molecular Biology, having authored 43 papers that have together received 322 indexed citations. Recurring topics across this work include Advanced Graph Neural Networks (12 papers), Topic Modeling (10 papers), Recommender Systems and Techniques (9 papers), Semantic Web and Ontologies (7 papers), Image and Object Detection Techniques (4 papers), Advanced Image and Video Retrieval Techniques (4 papers), Rough Sets and Fuzzy Logic (3 papers) and Caching and Content Delivery (3 papers). The work is most often cited by research in Artificial Intelligence (167 citations), Information Systems (98 citations), Computer Vision and Pattern Recognition (57 citations), Computer Science Applications (12 citations) and Media Technology (17 citations). Pu Li has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Yanbu Guo, Jinde Cao, Chaoyang Li, Dongming Zhou, David Bodoff, Suzhi Zhang, Yuncheng Jiang, Yong Tang, Yazhou Zhang and Boi Faltings. Their work appears in journals such as International Journal on Semantic Web and Information Systems, Engineering Applications of Artificial Intelligence, Applied Sciences, Electronics and ACM Transactions on Information Systems.

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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