Li Pan

4.0k citations
129 papers · 2.5k · 1 hit paper · h-index 27

Impact in

Papers in

Li Pan

120 papers receiving 2.5k citations

Li Pan's Hit Papers

Predicting Short-Term Traffic Flow by Long Short-Term Memory Recurrent Neural Network 2015 · 373 citations
3730+3+7Years since publication100200300

Peers

Li Pan
Comparison fields: 5 of 146
  • Transportation 287
  • Building and Construction 366
  • Artificial Intelligence 838
  • Statistical and Nonlinear Physics 307
  • Computer Vision and Pattern Recognition 394
Replace Jianguo Chen with:
Jianguo Chen China
Hao Zhu China
Weiwei Jiang China
Yong Zhang China
Huayi Wu China
Jun Zhao China
Bin Gao China
Ziwei Zhang China
Yi Liu China
Cheng Long Singapore
Li Pan relative to Jianguo Chen China Jianguo Chen's profile →
Citations per field
00.5×3.5×
Jianguo Chen · 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 129 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Predicting Short-Term Traffic Flow by Long Short-Term Memory Recurrent Neural Network
Hit paper breakdown →
2015373
2 2019231
3 2014217
4 202088
5 200586
6 201778
7 202159
8 202259
9 201456
10 201956
11 201954
12 201953
13 202053
14 201850
15 201546
16 201444
17 202043
18 201741
19 201739
20 201538

About Li Pan

Li Pan is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics, Computer Networks and Communications, Information Systems and Computer Vision and Pattern Recognition, having authored 129 papers that have together received 2.5k indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (30 papers), Advanced Graph Neural Networks (15 papers), Opinion Dynamics and Social Influence (15 papers), Topic Modeling (12 papers), Network Security and Intrusion Detection (8 papers), Traffic Prediction and Management Techniques (6 papers), Anomaly Detection Techniques and Applications (6 papers) and Access Control and Trust (6 papers). The work is most often cited by research in Transportation (287 citations), Building and Construction (366 citations), Artificial Intelligence (838 citations), Statistical and Nonlinear Physics (307 citations) and Computer Vision and Pattern Recognition (394 citations). Li Pan has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Peng Wu, Ping Yi, Yue Wu, Jianhua Li, Wenchao Li, Changqing Zou, Qun Niu, Ning Liu, Hefeng Wu and Bo Fang. Their work appears in journals such as IEEE Transactions on Neural Networks and Learning Systems, Computers & Security, IEEE Geoscience and Remote Sensing Letters, Marine and Petroleum Geology and Information Processing & Management.

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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