Runlong Yu
Impact in
- Information Systems top 5%
- Recommender Systems and Techniques
- Artificial Intelligence top 10%
- Advanced Graph Neural Networks
- Privacy-Preserving Technologies in Data
- Topic Modeling
- Intelligent Tutoring Systems and Adaptive Learning
Papers in
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- Computational Physics and Python Applications 2
- Advanced Graph Neural Networks 2
-
- Recommender Systems and Techniques 9
- Co-authors
- Enhong Chen (10 shared papers)Qi Liu (8 shared papers)Mingyue Cheng (4 shared papers)Likang Wu (1 shared paper)Hui Xiong (3 shared papers)Hengshu Zhu (3 shared papers)Zaixi Zhang (1 shared paper)Chao Wang (1 shared paper)
- Journals
- IEEE Transactions on Knowledge and Data Engineering (3 papers)Plant Biology (1 paper)Communications of the ACM (1 paper)Knowledge and Information Systems (1 paper)Expert Systems with Applications (1 paper)
- Partner nations
- ChinaUnited StatesNetherlands
In The Last Decade
Runlong Yu
20 papers receiving 218 citations
Peers
Comparison fields: 5 of 53
- Information Systems 114
- Artificial Intelligence 131
- Computer Science Applications 19
- Computational Mathematics 2
- Management Science and Operations Research 37
Countries citing papers authored by Runlong Yu
This map shows the geographic impact of Runlong 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 Runlong Yu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Runlong Yu more than expected).
Fields of papers citing papers by Runlong Yu
This network shows the impact of papers produced by Runlong 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 Runlong Yu. The network helps show where Runlong Yu may publish in the future.
Co-authors
The 25 scholars most cited alongside Runlong 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 24 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2018 | 36 | |
| 2 | 2023 | 35 | |
| 3 | 2019 | 25 | |
| 4 | 2020 | 23 | |
| 5 | 2021 | 14 | |
| 6 | 2022 | 13 | |
| 7 | 2021 | 12 | |
| 8 | 2022 | 11 | |
| 9 | 2022 | 10 | |
| 10 | 2019 | 9 | |
| 11 | 2019 | 8 | |
| 12 | 2022 | 7 | |
| 13 | 2023 | 5 | |
| 14 | 2024 | 4 | |
| 15 | 2025 | 3 | |
| 16 | 2024 | 1 | |
| 17 | 2023 | 1 | |
| 18 | 2024 | 1 | |
| 19 | 2021 | 1 | |
| 20 | 2025 | 1 |
About Runlong Yu
Runlong Yu is a scholar working on Artificial Intelligence, Information Systems, Signal Processing, Materials Chemistry and Social Psychology, having authored 24 papers that have together received 220 indexed citations. Recurring topics across this work include Recommender Systems and Techniques (9 papers), Machine Learning in Materials Science (3 papers), Mental Health via Writing (2 papers), Computational Physics and Python Applications (2 papers), Digital Marketing and Social Media (2 papers), Advanced Graph Neural Networks (2 papers), Intellectual Property and Patents (2 papers) and Mobile Crowdsensing and Crowdsourcing (2 papers). The work is most often cited by research in Information Systems (114 citations), Artificial Intelligence (131 citations), Computer Science Applications (19 citations), Computational Mathematics (2 citations) and Management Science and Operations Research (37 citations). Runlong Yu has collaborated with scholars based in China, United States and Netherlands. Frequent co-authors include Enhong Chen, Qi Liu, Mingyue Cheng, Likang Wu, Hui Xiong, Hengshu Zhu, Zaixi Zhang, Chao Wang, Le Wu and Yunzhou Zhang. Their work appears in journals such as IEEE Transactions on Knowledge and Data Engineering, Plant Biology, Communications of the ACM, Knowledge and Information Systems and Expert Systems with Applications.
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.