Chuan Chen
Impact in
- Computational Mathematics top 1%
- Artificial Intelligence top 0.5%
- Privacy-Preserving Technologies in Data
- Advanced Graph Neural Networks
- Cryptography and Data Security
- Domain Adaptation and Few-Shot Learning
Papers in
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- Tensor decomposition and applications 14
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- Advanced Graph Neural Networks 35
- Privacy-Preserving Technologies in Data 20
Chuan Chen
155 papers receiving 3.2k citations
Hit Papers
Peers
Comparison fields: 5 of 173
- Computational Mathematics 123
- Artificial Intelligence 1.6k
- Information Systems 946
- Statistical and Nonlinear Physics 363
- Computer Vision and Pattern Recognition 543
Countries citing papers authored by Chuan Chen
This map shows the geographic impact of Chuan 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 Chuan Chen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chuan Chen more than expected).
Fields of papers citing papers by Chuan Chen
This network shows the impact of papers produced by Chuan 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 Chuan Chen. The network helps show where Chuan Chen may publish in the future.
Co-authors
The 25 scholars most cited alongside Chuan Chen, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2024 | 0 | |
| 3 | 2024 | 1 | |
| 4 | 2024 | 3 | |
| 5 | 2024 | 5 | |
| 6 | 2023 | 4 | |
| 7 | 2023 | 9 | |
| 8 | 2023 | 4 | |
| 9 | 2023 | 28 | |
| 10 | 2023 | 5 | |
| 11 | 2023 | 2 | |
| 12 | 2023 | 10 | |
| 13 | 2022 | 18 | |
| 14 | 2022 | 66 | |
| 15 | 2021 | 38 | |
| 16 | 2020 | 16 | |
| 17 | 2019 | 6 | |
| 18 | 2019 | 124 | |
| 19 | Learning Semantic Representations for Unsupervised Domain Adaptation | 2018 | 218 |
| 20 | Learning Mixture Models for Classification with Energy Combination = Učenje mešanih modelov računalniške klasifikacije | 2007 | 2 |
About Chuan Chen
Chuan Chen is a scholar working on Computational Mathematics, Artificial Intelligence, Statistical and Nonlinear Physics, Computer Vision and Pattern Recognition and Information Systems, having authored 170 papers that have together received 3.2k indexed citations. Recurring topics across this work include Advanced Graph Neural Networks (35 papers), Complex Network Analysis Techniques (21 papers), Privacy-Preserving Technologies in Data (20 papers), Tensor decomposition and applications (14 papers), Recommender Systems and Techniques (11 papers), Cerebrovascular and Carotid Artery Diseases (10 papers), Sparse and Compressive Sensing Techniques (9 papers) and Blockchain Technology Applications and Security (8 papers). The work is most often cited by research in Computational Mathematics (123 citations), Artificial Intelligence (1.6k citations), Information Systems (946 citations), Statistical and Nonlinear Physics (363 citations) and Computer Vision and Pattern Recognition (543 citations). Chuan Chen has collaborated with scholars based in China, Hong Kong and United States. Frequent co-authors include Zibin Zheng, Fanghua Ye, Jiajing Wu, Huawei Huang, Yuzheng Li, Yan Qiang, Nan Liu, Shaoan Xie, Liang Chen and Wuhui Chen. Their work appears in journals such as Information Sciences, IEEE Transactions on Neural Networks and Learning Systems, ACM Transactions on Knowledge Discovery from Data, Knowledge-Based 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.