Ling Chen

297 papers receiving 5.1k citations

Ling Chen's Hit Papers

LCARS 2013 · 250 citations
2500+4+8Years since publication50100150200250

Peers

Ling Chen
Comparison fields: 5 of 175
  • Computational Mathematics 78
  • Transportation 782
  • Information Systems 1.5k
  • Artificial Intelligence 2.0k
  • Statistical and Nonlinear Physics 762
Replace Xia Hu with:
Xia Hu United States
Chao Zhang China
Bin Cui China
Jing Gao United States
Tao Li United States
Yong Liu China
Junjie Wu China
Christopher Leckie Australia
Mao Ye China
Jianxin Li China
Ling Chen relative to Xia Hu United States Xia Hu's profile →
Citations per field
00.5×1.5×
Xia Hu · 1×
Citations per year

Countries citing papers authored by Ling Chen

Since Specialization
Citations

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

Fields of papers citing papers by Ling Chen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 331 papers — load more, or switch the sort, to bring in the rest.

#Work
1
LCARS
Hit paper breakdown →
2013250
2 2017222
3 1987167
4 2009156
5 2012130
6 2021122
7 2015107
8 2015105
9 2012105
10 2015104
11 202298
12 201497
13 201492
14 200591
15 202181
16 201481
17 200475
18 201667
19 201563
20 201862

About Ling Chen

Ling Chen is a scholar working on Artificial Intelligence, Information Systems, Computer Vision and Pattern Recognition, Computer Networks and Communications and Signal Processing, having authored 331 papers that have together received 5.3k indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (38 papers), Data Mining Algorithms and Applications (36 papers), Advanced Graph Neural Networks (29 papers), Data Management and Algorithms (25 papers), Rough Sets and Fuzzy Logic (25 papers), Topic Modeling (23 papers), Recommender Systems and Techniques (22 papers) and Advanced Database Systems and Queries (18 papers). The work is most often cited by research in Computational Mathematics (78 citations), Transportation (782 citations), Information Systems (1.5k citations), Artificial Intelligence (2.0k citations) and Statistical and Nonlinear Physics (762 citations). Ling Chen has collaborated with scholars based in China, Australia and United States. Frequent co-authors include Hongzhi Yin, Chengqi Zhang, Bin Cui, Zhiting Hu, Xiaofang Zhou, Yizhou Sun, Abhishek Roy, Weiqing Wang, Gencai Chen and Guansong Pang. Their work appears in journals such as IEEE Transactions on Knowledge and Data Engineering, Information Sciences, Applied Intelligence, IEEE Transactions on Neural Networks and Learning Systems and Neurocomputing.

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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