Rui Men
Impact in
- Artificial Intelligence top 5%
- Topic Modeling
- Natural Language Processing Techniques
- Sentiment Analysis and Opinion Mining
- Advanced Graph Neural Networks
- Domain Adaptation and Few-Shot Learning
- Advanced Text Analysis Techniques
- Text and Document Classification Technologies
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- Multimodal Machine Learning Applications
Papers in
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- Natural Language Processing Techniques 4
- Topic Modeling 3
- Privacy-Preserving Technologies in Data 3
- Domain Adaptation and Few-Shot Learning 2
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- Blockchain Technology Applications and Security 3
- Co-authors
- Zhi Jin (3 shared papers)Yan Xu (2 shared papers)Lu Zhang (2 shared papers)Rui Yan (2 shared papers)Lili Mou (2 shared papers)Ge Li (2 shared papers)Junyang Lin (5 shared papers)Hongxia Yang (3 shared papers)
In The Last Decade
Rui Men
15 papers receiving 300 citations
Peers
Comparison fields: 5 of 55
- Artificial Intelligence 239
- Computer Vision and Pattern Recognition 81
- Information Systems 53
- Computer Science Applications 5
- Computer Networks and Communications 18
Countries citing papers authored by Rui Men
This map shows the geographic impact of Rui Men'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 Rui Men with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rui Men more than expected).
Fields of papers citing papers by Rui Men
This network shows the impact of papers produced by Rui Men. 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 Rui Men. The network helps show where Rui Men may publish in the future.
Co-authors
The 25 scholars most cited alongside Rui Men, 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 | 2016 | 209 | |
| 2 | 2021 | 23 | |
| 3 | 2016 | 16 | |
| 4 | Recognizing Entailment and Contradiction by Tree-based Convolution | 2015 | 12 |
| 5 | 2021 | 11 | |
| 6 | 2024 | 7 | |
| 7 | Exploring Sparse Expert Models and Beyond | 2021 | 4 |
| 8 | 2023 | 4 | |
| 9 | 2019 | 4 | |
| 10 | 2019 | 4 | |
| 11 | 2023 | 4 | |
| 12 | 2025 | 4 | |
| 13 | 2020 | 3 | |
| 14 | 2024 | 3 | |
| 15 | 2024 | 3 | |
| 16 | 2025 | 0 | |
| 17 | 2022 | 0 |
About Rui Men
Rui Men is a scholar working on Artificial Intelligence, Information Systems, Electrical and Electronic Engineering, Computer Networks and Communications and Computer Vision and Pattern Recognition, having authored 17 papers that have together received 311 indexed citations. Recurring topics across this work include Vehicular Ad Hoc Networks (VANETs) (4 papers), Natural Language Processing Techniques (4 papers), Blockchain Technology Applications and Security (3 papers), IoT and Edge/Fog Computing (3 papers), Topic Modeling (3 papers), Privacy-Preserving Technologies in Data (3 papers), Opportunistic and Delay-Tolerant Networks (2 papers) and Domain Adaptation and Few-Shot Learning (2 papers). The work is most often cited by research in Artificial Intelligence (239 citations), Computer Vision and Pattern Recognition (81 citations), Information Systems (53 citations), Computer Science Applications (5 citations) and Computer Networks and Communications (18 citations). Rui Men has collaborated with scholars based in China, Malaysia and Japan. Frequent co-authors include Zhi Jin, Yan Xu, Lu Zhang, Rui Yan, Lili Mou, Ge Li, Junyang Lin, Hongxia Yang, Xiumei Fan and Yang An. Their work appears in journals such as Information Resources Management Journal, IEEE Access, Ad Hoc Networks, IEEE Transactions on Network Science and Engineering and Scientific Reports.
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.