Kai Chen
- Computer Networks and Communications top 0.1%
- Software-Defined Networks and 5G 78
- Interconnection Networks and Systems 32
- Network Traffic and Congestion Control 31
- Caching and Content Delivery 28
- Network Security and Intrusion Detection 21
-
- Advanced Neural Network Applications 34
- Information Systems top 0.1%
- Cloud Computing and Resource Management 94
- Artificial Intelligence top 0.2%
- Internet Traffic Analysis and Secure E-voting 20
- Hardware and Architecture top 1%
- Cited by
- Computer Networks and CommunicationsComputer Vision and Pattern RecognitionInformation Systems
- Journals
- IEEE/ACM Transactions on Networking (20 papers)ACM Transactions on Intelligent Systems and Technology (8 papers)Neurocomputing (3 papers)
- Partner nations
- ChinaHong KongUnited States
In The Last Decade
Kai Chen
371 papers receiving 11.9k citations
Hit Papers
Peers
Comparison fields: 5 of 195
- Computer Networks and Communications 5.3k
- Computer Vision and Pattern Recognition 3.6k
- Information Systems 2.8k
- Artificial Intelligence 3.1k
- Hardware and Architecture 504
Countries citing papers authored by Kai Chen
This map shows the geographic impact of Kai 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 Kai Chen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kai Chen more than expected).
Fields of papers citing papers by Kai Chen
This network shows the impact of papers produced by Kai 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 Kai Chen. The network helps show where Kai Chen may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Kai 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 | 8 | |
| 2 | 2025 | 6 | |
| 3 | 2024 | 0 | |
| 4 | 2024 | 1 | |
| 5 | 2024 | 13 | |
| 6 | 2024 | 19 | |
| 7 | 2024 | 2 | |
| 8 | 2024 | 0 | |
| 9 | 2024 | 1 | |
| 10 | 2023 | 21 | |
| 11 | 2023 | 2 | |
| 12 | 2023 | 23 | |
| 13 | 2022 | 4 | |
| 14 | 2022 | 2 | |
| 15 | 2020 | 23 | |
| 16 | 2020 | 0 | |
| 17 | 2020 | 5 | |
| 18 | 2019 | 16 | |
| 19 | 2017 | 180 | |
| 20 | Drug Discovery in Post-genome Era:Trend and Practice | 2004 | 3 |
About Kai Chen
Kai Chen is a scholar working on Computer Networks and Communications, Information Systems and Hardware and Architecture, having authored 420 papers that have together received 12.4k indexed citations. Recurring topics across this work include Cloud Computing and Resource Management (94 papers), Software-Defined Networks and 5G (78 papers), Advanced Neural Network Applications (34 papers), Interconnection Networks and Systems (32 papers), Network Traffic and Congestion Control (31 papers), Caching and Content Delivery (28 papers), Network Security and Intrusion Detection (21 papers) and Internet Traffic Analysis and Secure E-voting (20 papers). The work is most often cited by research in Computer Networks and Communications (5.3k citations), Computer Vision and Pattern Recognition (3.6k citations) and Information Systems (2.8k citations). Kai Chen has collaborated with scholars based in China, Hong Kong and United States. Frequent co-authors include Dahua Lin, Kai Niu, Wei Bai, Jiangmiao Pang, Li Chen, Wanli Ouyang, Jianping Shi, Huajun Feng, Klara Nahrstedt and Chen Change Loy. Their work appears in journals such as IEEE/ACM Transactions on Networking, ACM Transactions on Intelligent Systems and Technology, Neurocomputing, Electronics and IEEE Transactions on Cloud Computing.
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.