Jun Xia
Impact in
- Artificial Intelligence top 5%
- Advanced Graph Neural Networks
- Topic Modeling
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- Hydrology and Watershed Management Studies
Papers in
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- Advanced Graph Neural Networks 8
- Machine Learning and Data Classification 3
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- Single-cell and spatial transcriptomics 2
- Co-authors
- Stan Z. Li (15 shared papers)Lirong Wu (8 shared papers)Bozhen Hu (5 shared papers)Cheng Tan (9 shared papers)Siyuan Li (3 shared papers)Zhangyang Gao (4 shared papers)Yongjie Xu (3 shared papers)Sidong Zeng (5 shared papers)
In The Last Decade
Jun Xia
26 papers receiving 488 citations
Jun Xia's Hit Papers
Peers
Comparison fields: 5 of 94
- Artificial Intelligence 232
- Water Science and Technology 59
- Environmental Engineering 58
- Statistical and Nonlinear Physics 44
- Computer Vision and Pattern Recognition 69
Countries citing papers authored by Jun Xia
This map shows the geographic impact of Jun Xia'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 Jun Xia with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jun Xia more than expected).
Fields of papers citing papers by Jun Xia
This network shows the impact of papers produced by Jun Xia. 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 Jun Xia. The network helps show where Jun Xia may publish in the future.
Co-authors
The 25 scholars most cited alongside Jun Xia, 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 29 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation Hit paper breakdown → | 2022 | 147 |
| 2 | 2023 | 81 | |
| 3 | 2022 | 42 | |
| 4 | 2022 | 33 | |
| 5 | 2023 | 29 | |
| 6 | 2023 | 28 | |
| 7 | 2023 | 17 | |
| 8 | 2023 | 14 | |
| 9 | 2022 | 14 | |
| 10 | 2022 | 14 | |
| 11 | 2023 | 12 | |
| 12 | 2022 | 9 | |
| 13 | 2022 | 9 | |
| 14 | The role of Fbxo5 in the development of human malignant tumors. | 2022 | 7 |
| 15 | 2023 | 6 | |
| 16 | 2022 | 6 | |
| 17 | 2013 | 3 | |
| 18 | 2023 | 3 | |
| 19 | 2023 | 3 | |
| 20 | 2022 | 3 |
About Jun Xia
Jun Xia is a scholar working on Artificial Intelligence, Molecular Biology, Global and Planetary Change, Water Science and Technology and Computer Vision and Pattern Recognition, having authored 29 papers that have together received 491 indexed citations. Recurring topics across this work include Advanced Graph Neural Networks (8 papers), Flood Risk Assessment and Management (5 papers), Hydrology and Watershed Management Studies (5 papers), Hydrological Forecasting Using AI (3 papers), Climate variability and models (3 papers), Human Pose and Action Recognition (3 papers), Machine Learning and Data Classification (3 papers) and Single-cell and spatial transcriptomics (2 papers). The work is most often cited by research in Artificial Intelligence (232 citations), Water Science and Technology (59 citations), Environmental Engineering (58 citations), Statistical and Nonlinear Physics (44 citations) and Computer Vision and Pattern Recognition (69 citations). Jun Xia has collaborated with scholars based in China, Australia and Germany. Frequent co-authors include Stan Z. Li, Lirong Wu, Bozhen Hu, Cheng Tan, Siyuan Li, Zhangyang Gao, Yongjie Xu, Sidong Zeng, Yanqiao Zhu and Yuanqi Du. Their work appears in journals such as The Visual Computer, Journal of Hydrology, Urban Climate, Cell Death and Disease and Journal of Hydrology Regional Studies.
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.