Peng Jin

116 total papers · 834 total citations
69 papers, 491 citations indexed

About

Peng Jin is a scholar working on Artificial Intelligence, Electrical and Electronic Engineering and Automotive Engineering. According to data from OpenAlex, Peng Jin has authored 69 papers receiving a total of 491 indexed citations (citations by other indexed papers that have themselves been cited), including 41 papers in Artificial Intelligence, 13 papers in Electrical and Electronic Engineering and 12 papers in Automotive Engineering. Recurrent topics in Peng Jin's work include Topic Modeling (26 papers), Natural Language Processing Techniques (25 papers) and Advanced Battery Technologies Research (12 papers). Peng Jin is often cited by papers focused on Topic Modeling (26 papers), Natural Language Processing Techniques (25 papers) and Advanced Battery Technologies Research (12 papers). Peng Jin collaborates with scholars based in China, United Kingdom and Singapore. Peng Jin's co-authors include Yunfang Wu, Xingyuan Chen, Yunqing Xia, Lin Peng, John Carroll, Zhenpo Wang, Jun Zeng, Mingwei Sun, Yu Wang and Hua Jiang and has published in prestigious journals such as Water Research, Journal of The Electrochemical Society and IEEE Access.

In The Last Decade

Peng Jin

59 papers receiving 462 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Peng Jin 266 90 87 54 37 69 491
Saima Hassan 177 0.7× 150 1.7× 24 0.3× 54 1.0× 50 1.4× 46 444
Yang Wang 91 0.3× 191 2.1× 115 1.3× 30 0.6× 43 1.2× 70 548
Tao Xu 124 0.5× 115 1.3× 188 2.2× 48 0.9× 128 3.5× 64 515
N. Suthanthira Vanitha 161 0.6× 153 1.7× 52 0.6× 63 1.2× 61 1.6× 76 542
Rosli Besar 102 0.4× 116 1.3× 27 0.3× 201 3.7× 29 0.8× 57 525
Jiangtao Wang 148 0.6× 60 0.7× 38 0.4× 43 0.8× 41 1.1× 38 407
Enrico De Santis 171 0.6× 265 2.9× 24 0.3× 30 0.6× 167 4.5× 42 462
Pi-Yun Chen 111 0.4× 107 1.2× 48 0.6× 79 1.5× 58 1.6× 46 409
Mayukha Pal 74 0.3× 151 1.7× 30 0.3× 26 0.5× 82 2.2× 46 457
Steve A. Adeshina 179 0.7× 64 0.7× 9 0.1× 121 2.2× 14 0.4× 81 473

Countries citing papers authored by Peng Jin

Since Specialization
Citations

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

Fields of papers citing papers by Peng Jin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Peng Jin

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

All Works

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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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