Yujie Mo
Impact in
- Artificial Intelligence top 5%
- Advanced Graph Neural Networks
- Domain Adaptation and Few-Shot Learning
- Text and Document Classification Technologies
Papers in
-
- Advanced Graph Neural Networks 10
- Domain Adaptation and Few-Shot Learning 2
- Text and Document Classification Technologies 2
-
- Recommender Systems and Techniques 4
- Co-authors
- Xiaofeng Zhu (12 shared papers)Liang Peng (7 shared papers)Xiaoshuang Shi (6 shared papers)Jie Xu (2 shared papers)Jie Xu (1 shared paper)Yazhou Ren (1 shared paper)Chao Li (1 shared paper)Heng Tao Shen (4 shared papers)
- Journals
- IEEE Transactions on Neural Networks and Learning Systems (3 papers)Information Fusion (2 papers)Information Processing & Management (1 paper)IEEE Transactions on Knowledge and Data Engineering (1 paper)Journal of Chemical Theory and Computation (1 paper)
- Partner nations
- ChinaNew ZealandGermany
In The Last Decade
Yujie Mo
16 papers receiving 364 citations
Peers
Comparison fields: 5 of 55
- Computational Mathematics 5
- Artificial Intelligence 254
- Computer Vision and Pattern Recognition 140
- Statistical and Nonlinear Physics 43
- Information Systems 62
Countries citing papers authored by Yujie Mo
This map shows the geographic impact of Yujie Mo'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 Yujie Mo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yujie Mo more than expected).
Fields of papers citing papers by Yujie Mo
This network shows the impact of papers produced by Yujie Mo. 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 Yujie Mo. The network helps show where Yujie Mo may publish in the future.
Co-authors
The 25 scholars most cited alongside Yujie Mo, 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 | 2022 | 81 | |
| 2 | 2022 | 75 | |
| 3 | 2022 | 60 | |
| 4 | 2023 | 49 | |
| 5 | 2023 | 43 | |
| 6 | 2022 | 15 | |
| 7 | 2024 | 8 | |
| 8 | 2024 | 6 | |
| 9 | 2022 | 6 | |
| 10 | 2024 | 6 | |
| 11 | 2022 | 4 | |
| 12 | 2022 | 4 | |
| 13 | 2024 | 3 | |
| 14 | 2023 | 2 | |
| 15 | 2024 | 1 | |
| 16 | 2021 | 1 | |
| 17 | 2025 | 0 | |
| 18 | 2025 | 0 | |
| 19 | 2024 | 0 |
About Yujie Mo
Yujie Mo is a scholar working on Artificial Intelligence, Information Systems, Computer Vision and Pattern Recognition, Molecular Biology and Electrical and Electronic Engineering, having authored 19 papers that have together received 364 indexed citations. Recurring topics across this work include Advanced Graph Neural Networks (10 papers), Recommender Systems and Techniques (4 papers), Domain Adaptation and Few-Shot Learning (2 papers), Epigenetics and DNA Methylation (2 papers), Graph Theory and Algorithms (2 papers), Text and Document Classification Technologies (2 papers), Clay minerals and soil interactions (1 paper) and Electric Vehicles and Infrastructure (1 paper). The work is most often cited by research in Computational Mathematics (5 citations), Artificial Intelligence (254 citations), Computer Vision and Pattern Recognition (140 citations), Statistical and Nonlinear Physics (43 citations) and Information Systems (62 citations). Yujie Mo has collaborated with scholars based in China, New Zealand and Germany. Frequent co-authors include Xiaofeng Zhu, Liang Peng, Xiaoshuang Shi, Jie Xu, Jie Xu, Yazhou Ren, Chao Li, Heng Tao Shen, Jiangzhang Gan and Rongyao Hu. Their work appears in journals such as IEEE Transactions on Neural Networks and Learning Systems, Information Fusion, Information Processing & Management, IEEE Transactions on Knowledge and Data Engineering and Journal of Chemical Theory and Computation.
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.