Shuo Yu
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- Complex Network Analysis Techniques 29
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- scientometrics and bibliometrics research 12
- Artificial Intelligence top 5%
- Advanced Graph Neural Networks 26
- Topic Modeling 9
- Anomaly Detection Techniques and Applications 5
- Information Systems top 2%
- Recommender Systems and Techniques 9
- Computer Science Applications top 10%
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- Bioinformatics and Genomic Networks 13
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- Network Security and Intrusion Detection 5
- Cited by
- Statistical and Nonlinear PhysicsStatistics, Probability and UncertaintyArtificial Intelligence
- Journals
- IEEE Transactions on Computational Social Systems (7 papers)IEEE Access (3 papers)IEEE Transactions on Consumer Electronics (2 papers)
- Partner nations
- ChinaAustraliaUnited States
In The Last Decade
Shuo Yu
69 papers receiving 856 citations
Peers
Comparison fields: 5 of 109
- Statistical and Nonlinear Physics 299
- Statistics, Probability and Uncertainty 111
- Artificial Intelligence 419
- Information Systems 265
- Computer Science Applications 45
Countries citing papers authored by Shuo Yu
This map shows the geographic impact of Shuo 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 Shuo Yu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shuo Yu more than expected).
Fields of papers citing papers by Shuo Yu
This network shows the impact of papers produced by Shuo 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 Shuo Yu. The network helps show where Shuo Yu may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Shuo 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
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2025 | 0 | |
| 3 | 2025 | 1 | |
| 4 | 2025 | 0 | |
| 5 | 2024 | 2 | |
| 6 | 2024 | 2 | |
| 7 | 2024 | 3 | |
| 8 | 2024 | 3 | |
| 9 | 2023 | 0 | |
| 10 | 2023 | 3 | |
| 11 | 2023 | 6 | |
| 12 | 2023 | 19 | |
| 13 | 2023 | 2 | |
| 14 | 2023 | 15 | |
| 15 | 2022 | 17 | |
| 16 | 2022 | 10 | |
| 17 | 2021 | 31 | |
| 18 | 2020 | 25 | |
| 19 | 2019 | 3 | |
| 20 | 2016 | 30 |
About Shuo Yu
Shuo Yu is a scholar working on Statistical and Nonlinear Physics, Statistics, Probability and Uncertainty, Artificial Intelligence, Information Systems and Modeling and Simulation, having authored 79 papers that have together received 879 indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (29 papers), Advanced Graph Neural Networks (26 papers), Bioinformatics and Genomic Networks (13 papers), scientometrics and bibliometrics research (12 papers), Topic Modeling (9 papers), Recommender Systems and Techniques (9 papers), Network Security and Intrusion Detection (5 papers) and Anomaly Detection Techniques and Applications (5 papers). The work is most often cited by research in Statistical and Nonlinear Physics (299 citations), Statistics, Probability and Uncertainty (111 citations), Artificial Intelligence (419 citations), Information Systems (265 citations) and Computer Science Applications (45 citations). Shuo Yu has collaborated with scholars based in China, Australia and United States. Frequent co-authors include Feng Xia, Xiangjie Kong, Jiaying Liu, Wei Wang, Ivan Lee, Teshome Megersa Bekele, Bo Xu, Xiaomei Bai, Huizhen Jiang and Amr Tolba. Their work appears in journals such as IEEE Transactions on Computational Social Systems, IEEE Access, IEEE Transactions on Consumer Electronics, Computer Science Review and IEEE Transactions on Neural Networks and Learning Systems.
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.