Qingfeng Tan
Impact in
- Information Systems top 5%
- Blockchain Technology Applications and Security
- Spam and Phishing Detection
- Cybercrime and Law Enforcement Studies
- Signal Processing top 10%
- Advanced Malware Detection Techniques
Papers in
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- Internet Traffic Analysis and Secure E-voting 10
- Cryptography and Data Security 3
- Advanced Graph Neural Networks 3
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- Spam and Phishing Detection 5
- Blockchain Technology Applications and Security 4
- Co-authors
- Jinqiao Shi (12 shared papers)Xuebin Wang (11 shared papers)Yue Gao (4 shared papers)Zhihong Tian (4 shared papers)Binxing Fang (3 shared papers)Peng Zhang (4 shared papers)Zhao Li (3 shared papers)Can Zhao (1 shared paper)
In The Last Decade
Qingfeng Tan
25 papers receiving 278 citations
Peers
Comparison fields: 5 of 51
- Information Systems 141
- Signal Processing 64
- Computer Networks and Communications 122
- Artificial Intelligence 161
- Statistical and Nonlinear Physics 25
Countries citing papers authored by Qingfeng Tan
This map shows the geographic impact of Qingfeng Tan'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 Qingfeng Tan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Qingfeng Tan more than expected).
Fields of papers citing papers by Qingfeng Tan
This network shows the impact of papers produced by Qingfeng Tan. 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 Qingfeng Tan. The network helps show where Qingfeng Tan may publish in the future.
Co-authors
The 25 scholars most cited alongside Qingfeng Tan, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 28 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2018 | 82 | |
| 2 | 2019 | 25 | |
| 3 | 2021 | 24 | |
| 4 | 2016 | 20 | |
| 5 | 2022 | 16 | |
| 6 | 2017 | 13 | |
| 7 | 2014 | 12 | |
| 8 | 2023 | 12 | |
| 9 | 2018 | 12 | |
| 10 | 2016 | 11 | |
| 11 | 2018 | 10 | |
| 12 | 2022 | 9 | |
| 13 | 2020 | 7 | |
| 14 | 2021 | 6 | |
| 15 | 2018 | 6 | |
| 16 | 2024 | 4 | |
| 17 | 2023 | 4 | |
| 18 | 2018 | 3 | |
| 19 | 2022 | 3 | |
| 20 | 2013 | 2 |
About Qingfeng Tan
Qingfeng Tan is a scholar working on Artificial Intelligence, Information Systems, Computer Networks and Communications, Computer Vision and Pattern Recognition and Statistical and Nonlinear Physics, having authored 28 papers that have together received 289 indexed citations. Recurring topics across this work include Internet Traffic Analysis and Secure E-voting (10 papers), Network Security and Intrusion Detection (7 papers), Spam and Phishing Detection (5 papers), Blockchain Technology Applications and Security (4 papers), Caching and Content Delivery (3 papers), Peer-to-Peer Network Technologies (3 papers), Cryptography and Data Security (3 papers) and Advanced Graph Neural Networks (3 papers). The work is most often cited by research in Information Systems (141 citations), Signal Processing (64 citations), Computer Networks and Communications (122 citations), Artificial Intelligence (161 citations) and Statistical and Nonlinear Physics (25 citations). Qingfeng Tan has collaborated with scholars based in China, Australia and Canada. Frequent co-authors include Jinqiao Shi, Xuebin Wang, Yue Gao, Zhihong Tian, Binxing Fang, Peng Zhang, Zhao Li, Can Zhao, Tingwen Liu and Li Guo. Their work appears in journals such as World Wide Web, Neurocomputing, IEEE Internet of Things Journal, Health Information Science and Systems and 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.