Pei-Duo Yu
Impact in
- Health Informatics top 10%
- Modeling and Simulation top 10%
- COVID-19 epidemiological studies
Papers in
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- Complex Network Analysis Techniques 7
- Opinion Dynamics and Social Influence 4
-
- COVID-19 Digital Contact Tracing 3
- Co-authors
- Chee Wei Tan (21 shared papers)Ching Nam Hang (9 shared papers)Hung‐Lin Fu (5 shared papers)Michael Fuchs (1 shared paper)Roberto Morabito (1 shared paper)Jiasi Chen (1 shared paper)H. Vincent Poor (2 shared papers)Guanrong Chen (1 shared paper)
In The Last Decade
Pei-Duo Yu
19 papers receiving 250 citations
Peers
Comparison fields: 5 of 59
- Health Informatics 14
- Modeling and Simulation 18
- Statistical and Nonlinear Physics 50
- Computer Science Applications 19
- Artificial Intelligence 75
Countries citing papers authored by Pei-Duo Yu
This map shows the geographic impact of Pei-Duo Yu'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 Pei-Duo Yu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pei-Duo Yu more than expected).
Fields of papers citing papers by Pei-Duo Yu
This network shows the impact of papers produced by Pei-Duo Yu. 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 Pei-Duo Yu. The network helps show where Pei-Duo Yu may publish in the future.
Co-authors
The 13 scholars most cited alongside Pei-Duo Yu, 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 22 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2024 | 45 | |
| 2 | 2024 | 26 | |
| 3 | 2015 | 20 | |
| 4 | 2022 | 20 | |
| 5 | 2023 | 18 | |
| 6 | 2025 | 17 | |
| 7 | 2016 | 16 | |
| 8 | 2023 | 16 | |
| 9 | 2018 | 16 | |
| 10 | 2024 | 12 | |
| 11 | 2023 | 10 | |
| 12 | 2017 | 7 | |
| 13 | 2017 | 7 | |
| 14 | 2025 | 6 | |
| 15 | 2020 | 6 | |
| 16 | Teaching Computational Thinking by Gamification of K-12 Mathematics: Mobile App Math Games in Mathematics and Computer Science Tournament | 2017 | 5 |
| 17 | 2022 | 4 | |
| 18 | 2018 | 2 | |
| 19 | 2024 | 2 | |
| 20 | 2026 | 0 |
About Pei-Duo Yu
Pei-Duo Yu is a scholar working on Statistical and Nonlinear Physics, Information Systems, Artificial Intelligence, Computer Networks and Communications and Sociology and Political Science, having authored 22 papers that have together received 255 indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (7 papers), Data-Driven Disease Surveillance (4 papers), Opinion Dynamics and Social Influence (4 papers), COVID-19 epidemiological studies (3 papers), COVID-19 Digital Contact Tracing (3 papers), Network Security and Intrusion Detection (3 papers), Misinformation and Its Impacts (3 papers) and Topic Modeling (3 papers). The work is most often cited by research in Health Informatics (14 citations), Modeling and Simulation (18 citations), Statistical and Nonlinear Physics (50 citations), Computer Science Applications (19 citations) and Artificial Intelligence (75 citations). Pei-Duo Yu has collaborated with scholars based in Taiwan, Singapore and Hong Kong. Frequent co-authors include Chee Wei Tan, Ching Nam Hang, Hung‐Lin Fu, Michael Fuchs, Roberto Morabito, Jiasi Chen, H. Vincent Poor, Guanrong Chen, Wenyi Zhang and Zheng Liang. Their work appears in journals such as IEEE Journal of Selected Topics in Signal Processing, IEEE Transactions on Artificial Intelligence, Big Data and Cognitive Computing, IEEE Access and IEEE Transactions on Signal and Information Processing over Networks.
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.