Li Pan

921 citations
33 papers · 340 · h-index 7

Impact in

Papers in

Li Pan

29 papers receiving 334 citations

Peers

Li Pan
Comparison fields: 5 of 82
  • Computer Networks and Communications 111
  • Computational Theory and Mathematics 64
  • Health Informatics 5
  • Industrial and Manufacturing Engineering 30
  • Management Information Systems 22
Replace Dana Simian with:
Dana Simian Romania
Volodymyr Vasyutynskyy Germany
Pascal Lafourcade France
Khalid Aloufi Saudi Arabia
Mehdi Esnaashari Iran
Divya Kumar India
A. Charan Kumari India
Li Pan relative to Dana Simian Romania Dana Simian's profile →
Citations per field
00.5×8.3×
Dana Simian · 1×
Citations per year

Countries citing papers authored by Li Pan

Since Specialization
Citations

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

Fields of papers citing papers by Li Pan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 33 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2018137
2 201945
3 201331
4 201920
5 202515
6 201815
7 20208
8 20086
9 20186
10 20186
11 20136
12 20245
13 20135
14 20125
15 20144
16 20243
17 20223
18 20243
19
A Bearing Fault Diagnosis Method Based on EEMD-SVD and FCM Clustering
20162
20 20092

About Li Pan

Li Pan is a scholar working on Computer Networks and Communications, Molecular Biology, Computational Theory and Mathematics, Information Systems and Artificial Intelligence, having authored 33 papers that have together received 340 indexed citations. Recurring topics across this work include Petri Nets in System Modeling (6 papers), Bioinformatics and Genomic Networks (4 papers), Business Process Modeling and Analysis (4 papers), Scheduling and Optimization Algorithms (3 papers), Protein Structure and Dynamics (3 papers), Formal Methods in Verification (3 papers), Computational Drug Discovery Methods (3 papers) and Machine Learning and ELM (2 papers). The work is most often cited by research in Computer Networks and Communications (111 citations), Computational Theory and Mathematics (64 citations), Health Informatics (5 citations), Industrial and Manufacturing Engineering (30 citations) and Management Information Systems (22 citations). Li Pan has collaborated with scholars based in China, United States and Sweden. Frequent co-authors include Xin Peng, Yan Zhang, Supeng Leng, Ke Zhang, Sabita Maharjan, Bo Yang, Zhi Ding, Meng Zhou, Junjie Ding and Dongsheng Cao. Their work appears in journals such as IEEE Access, Planta Medica, Nature Methods, Scientific Reports and Journal of Allergy and Clinical Immunology.

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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