Kailong Chen

19 papers receiving 577 citations

Peers

Kailong Chen
Comparison fields: 5 of 81
  • Information Systems 318
  • Surfaces, Coatings and Films 81
  • Computational Mathematics 6
  • Statistical and Nonlinear Physics 118
  • Artificial Intelligence 197
Replace Fangli Xu with:
Fangli Xu China
Yulong Gu China
Xinjun Wang China
Junxue Zhang China
Ziheng Yu China
Qiannan Zhu China
Jiali Yang China
Mario Boley Germany
Hongyu Lin China
Kailong Chen relative to Fangli Xu China Fangli Xu's profile →
Citations per field
00.5×10×16.2×
Fangli Xu · 1×
Citations per year

Countries citing papers authored by Kailong Chen

Since Specialization
Citations

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

Fields of papers citing papers by Kailong Chen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 23 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2012189
2 2012145
3 202459
4 201335
5 202429
6 202424
7 201320
8 202418
9 202218
10 202413
11 201412
12 201211
13 20138
14
Extreme Gradient Boosting [R package xgboost version 1.3.2.1]
20215
15 20243
16 20253
17 20242
18 20251
19 20191
20 20250

About Kailong Chen

Kailong Chen is a scholar working on Electrical and Electronic Engineering, Materials Chemistry, Information Systems, Statistical and Nonlinear Physics and Biomedical Engineering, having authored 23 papers that have together received 596 indexed citations. Recurring topics across this work include Advanced Sensor and Energy Harvesting Materials (5 papers), Complex Network Analysis Techniques (5 papers), Recommender Systems and Techniques (4 papers), Surface Modification and Superhydrophobicity (3 papers), HVDC Systems and Fault Protection (3 papers), Silicon Carbide Semiconductor Technologies (2 papers), Power Systems and Renewable Energy (2 papers) and Advanced Text Analysis Techniques (2 papers). The work is most often cited by research in Information Systems (318 citations), Surfaces, Coatings and Films (81 citations), Computational Mathematics (6 citations), Statistical and Nonlinear Physics (118 citations) and Artificial Intelligence (197 citations). Kailong Chen has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Tianqi Chen, Yong Yu, Guo‐qing Zheng, Ou Jin, Yong Yu, Weinan Zhang, Zheng Zhao, Dongzhi Chen, Xin Guo and Cong Feng. Their work appears in journals such as Chemical Engineering Journal, Langmuir, Materials Horizons, IEEE Transactions on Industry Applications and Carbohydrate Polymers.

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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