Jing Xiao
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- Complex Network Analysis Techniques 21
- Opinion Dynamics and Social Influence 12
- Human-Computer Interaction top 10%
- Control and Systems Engineering top 10%
- Robot Manipulation and Learning 11
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- Teleoperation and Haptic Systems 10
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- Advanced Graph Neural Networks 9
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- Soft Robotics and Applications 9
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- Tactile and Sensory Interactions 6
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- Bioinformatics and Genomic Networks 5
- Co-authors
- Xiao-Ke XuDangxiao WangYuru ZhangRoman MykhailyshynSiyuan LiuYuhang LiuTeng ZhouShanzhou� Huang
- Cited by
- Statistical and Nonlinear PhysicsHuman-Computer InteractionComputer Vision and Pattern Recognition
- Journals
- Nucleic Acids Research (1 paper)Nature Communications (1 paper)SHILAP Revista de lepidopterología (1 paper)
- Partner nations
- ChinaUnited StatesUkraine
In The Last Decade
Jing Xiao
79 papers receiving 679 citations
Peers
Comparison fields: 5 of 119
- Statistical and Nonlinear Physics 120
- Human-Computer Interaction 44
- Computer Vision and Pattern Recognition 92
- General Dentistry 8
- Control and Systems Engineering 99
Countries citing papers authored by Jing Xiao
This map shows the geographic impact of Jing Xiao'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 Jing Xiao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jing Xiao more than expected).
Fields of papers citing papers by Jing Xiao
This network shows the impact of papers produced by Jing Xiao. 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 Jing Xiao. The network helps show where Jing Xiao may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Jing Xiao, 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 | 3 | |
| 2 | 2025 | 0 | |
| 3 | 2025 | 3 | |
| 4 | 2025 | 0 | |
| 5 | 2025 | 0 | |
| 6 | 2024 | 2 | |
| 7 | 2024 | 2 | |
| 8 | 2024 | 0 | |
| 9 | 2024 | 6 | |
| 10 | 2024 | 11 | |
| 11 | 2024 | 7 | |
| 12 | 2024 | 6 | |
| 13 | 2023 | 27 | |
| 14 | 2023 | 16 | |
| 15 | 2023 | 60 | |
| 16 | 2023 | 0 | |
| 17 | 2022 | 3 | |
| 18 | How to Improve the Efficiency of Listening of Autonomous Learners through Strategy Training under Online Multimedia Environment | 2006 | 1 |
| 19 | 2006 | 5 | |
| 20 | On uncertainty handling for assembly tasks using robots | 1995 | 1 |
About Jing Xiao
Jing Xiao is a scholar working on Statistical and Nonlinear Physics, Human-Computer Interaction and Artificial Intelligence, having authored 89 papers that have together received 689 indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (21 papers), Opinion Dynamics and Social Influence (12 papers), Robot Manipulation and Learning (11 papers), Teleoperation and Haptic Systems (10 papers), Advanced Graph Neural Networks (9 papers), Soft Robotics and Applications (9 papers), Tactile and Sensory Interactions (6 papers) and Bioinformatics and Genomic Networks (5 papers). The work is most often cited by research in Statistical and Nonlinear Physics (120 citations), Human-Computer Interaction (44 citations) and Computer Vision and Pattern Recognition (92 citations). Jing Xiao has collaborated with scholars based in China, United States and Ukraine. Frequent co-authors include Xiao-Ke Xu, Dangxiao Wang, Yuru Zhang, Roman Mykhailyshyn, Siyuan Liu, Yuhang Liu, Teng Zhou, Shanzhou� Huang, Yufeng Ding and Hua Yu. Their work appears in journals such as Nucleic Acids Research, Nature Communications and SHILAP Revista de lepidopterología.
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.