Ling Chen

8.3k total citations · 1 hit paper
331 papers, 5.3k citations indexed

About

Ling Chen is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Ling Chen has authored 331 papers receiving a total of 5.3k indexed citations (citations by other indexed papers that have themselves been cited), including 129 papers in Artificial Intelligence, 89 papers in Information Systems and 71 papers in Computer Vision and Pattern Recognition. Recurrent topics in Ling Chen's work include Complex Network Analysis Techniques (38 papers), Data Mining Algorithms and Applications (36 papers) and Advanced Graph Neural Networks (29 papers). Ling Chen is often cited by papers focused on Complex Network Analysis Techniques (38 papers), Data Mining Algorithms and Applications (36 papers) and Advanced Graph Neural Networks (29 papers). Ling Chen collaborates with scholars based in China, Australia and United States. Ling Chen's co-authors include Hongzhi Yin, Chengqi Zhang, Zhiting Hu, Bin Cui, Xiaofang Zhou, Yizhou Sun, Abhishek Roy, Weiqing Wang, Gencai Chen and Guansong Pang and has published in prestigious journals such as Nature Communications, SHILAP Revista de lepidopterología and Analytical Chemistry.

In The Last Decade

Ling Chen

297 papers receiving 5.1k citations

Hit Papers

LCARS 2013 2026 2017 2021 2013 50 100 150 200 250
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. LCARS
    2013 250 citations Hongzhi Yin, Yizhou Sun et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Ling Chen China 38 2.0k 1.5k 1.1k 782 777 331 5.3k
Chao Zhang China 39 2.0k 1.0× 1.0k 0.7× 1.4k 1.3× 1.2k 1.6× 608 0.8× 262 5.7k
Jianxin Li China 44 2.6k 1.3× 1.3k 0.9× 822 0.7× 532 0.7× 1.4k 1.8× 398 7.1k
Junjie Wu China 37 2.6k 1.3× 912 0.6× 805 0.7× 463 0.6× 315 0.4× 191 6.1k
Hao Peng China 34 2.9k 1.4× 1.0k 0.7× 544 0.5× 517 0.7× 671 0.9× 177 5.1k
Cheng Yang China 28 3.5k 1.7× 1.2k 0.8× 1.0k 0.9× 325 0.4× 681 0.9× 148 6.5k
Jia Wu Australia 46 4.5k 2.2× 1.5k 0.9× 1.8k 1.6× 285 0.4× 780 1.0× 356 7.8k
Yong Liu China 37 1.7k 0.8× 1.9k 1.2× 744 0.7× 716 0.9× 706 0.9× 225 4.6k
Fan Zhou China 34 1.5k 0.8× 613 0.4× 474 0.4× 723 0.9× 453 0.6× 328 4.1k
Xiao Wang China 42 1.8k 0.9× 1.1k 0.7× 955 0.8× 462 0.6× 938 1.2× 394 7.3k
Jing Gao United States 37 4.0k 2.0× 1.2k 0.8× 1.1k 1.0× 393 0.5× 648 0.8× 164 6.4k

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-authorship network of co-authors of Ling Chen

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

All Works

20 of 20 papers shown
1.
He, Chengxuan, et al.. (2026). Light-driven restructuring generates nanoisland NiIr alloy for efficient methane dry reforming. Nature Communications. 17(1). 1730–1730.
2.
Chen, Ling, et al.. (2025). EmoVerse: Enhancing Multimodal Large Language Models for Affective Computing via Multitask Learning. Neurocomputing. 650. 130810–130810. 2 indexed citations
3.
Wu, Le, Jindong Wang, Ling Chen, et al.. (2025). Trustworthy Knowledge Discovery and Data Mining (TrustKDD). 6930–6932.
4.
Chen, Ling, et al.. (2024). “Sparse + Low-Rank” tensor completion approach for recovering images and videos. Signal Processing Image Communication. 127. 117152–117152. 1 indexed citations
5.
Zheng, Zhenhui, Ling Chen, Chenglin Wang, et al.. (2024). A robust and efficient citrus counting approach for large-scale unstructured orchards. Agricultural Systems. 215. 103867–103867. 8 indexed citations
6.
Wu, Peng, et al.. (2024). Research on public opinion effecting on stock price during crises based on model checking. Expert Systems with Applications. 249. 123442–123442. 1 indexed citations
7.
Li, Mingjie, et al.. (2024). In-Context Learning for Zero-shot Medical Report Generation. 8721–8730. 4 indexed citations
8.
Chen, Ling, et al.. (2024). An efficient and lightweight banana detection and localization system based on deep CNNs for agricultural robots. SHILAP Revista de lepidopterología. 9. 100550–100550. 3 indexed citations
9.
Chen, Ling, et al.. (2023). A low-rank and sparse enhanced Tucker decomposition approach for tensor completion. Applied Mathematics and Computation. 465. 128432–128432. 5 indexed citations
10.
Li, Li, et al.. (2023). HCTIRdeblur: A hybrid convolution-transformer network for single infrared image deblurring. Infrared Physics & Technology. 131. 104640–104640. 10 indexed citations
11.
12.
Xiang, Sheng, Dawei Cheng, Ruihui Zhao, et al.. (2023). Semi-supervised Credit Card Fraud Detection via Attribute-Driven Graph Representation. Proceedings of the AAAI Conference on Artificial Intelligence. 37(12). 14557–14565. 41 indexed citations
13.
Jiang, Zutao, Changlin Li, Xiaojun Chang, et al.. (2023). Dynamic Slimmable Denoising Network. IEEE Transactions on Image Processing. 32. 1583–1598. 13 indexed citations
14.
Fang, Meng, et al.. (2021). Reinforcement Learning With Multiple Relational Attention for Solving Vehicle Routing Problems. IEEE Transactions on Cybernetics. 52(10). 11107–11120. 81 indexed citations
15.
Zhan, Kun, et al.. (2018). Adaptive Structure Discovery for Multimedia Analysis Using Multiple Features. IEEE Transactions on Cybernetics. 49(5). 1826–1834. 41 indexed citations
16.
Wang, Weiqing, Hongzhi Yin, Ling Chen, et al.. (2017). ST-SAGE. ACM Transactions on Intelligent Systems and Technology. 8(3). 1–25. 23 indexed citations
17.
Pang, Guansong, Longbing Cao, & Ling Chen. (2016). Outlier detection in complex categorical data by modelling the feature value couplings. UTS ePRESS (University of Technology Sydney). 1902–1908. 40 indexed citations
18.
Chen, Ling, Marcus Gallagher, Bernard Pailthorpe, et al.. (2012). Introducing Cloud Computing Topics in Curricula. European Child & Adolescent Psychiatry. 23(3). 315–324. 15 indexed citations
19.
Chen, Ling. (2006). Ant Colony Algorithm for 0-1 Knapsack Problem. Jisuanji gongcheng. 4 indexed citations
20.
Li, Zhu, et al.. (2005). Path-Based Automatic Test Data Generation Approach. Kongzhi yu juece. 4 indexed citations

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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