Yaochen Xie

18 papers receiving 1.0k citations

Yaochen Xie's Hit Papers

Self-Supervised Learning of Graph Neural Networks: A Unified Review 2022 · 234 citations
2340+3+6Years since publication100200300400500

Peers

Yaochen Xie
Comparison fields: 5 of 115
  • Health, Toxicology and Mutagenesis 445
  • Environmental Engineering 253
  • Atmospheric Science 270
  • Artificial Intelligence 224
  • Automotive Engineering 82
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Da Gao China
Zhenyi Chen China
Guang Shi China
Shaofu Lin China
Xianneng Li China
Giorgio Corani Switzerland
Pengfei Li China
Yanyan Yang China
Jiwu Jing China
Liqun Liu China
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Countries citing papers authored by Yaochen Xie

Since Specialization
Citations

This map shows the geographic impact of Yaochen Xie'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 Yaochen Xie with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yaochen Xie more than expected).

Fields of papers citing papers by Yaochen Xie

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yaochen Xie. 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 Yaochen Xie. The network helps show where Yaochen Xie may publish in the future.

Co-authors

The 25 scholars most cited alongside Yaochen Xie, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Yaochen Xie Line = papers co-authored together Yaochen Xie links everyone, so they are left out of the graph.

All Works

19 of 19 papers shown
#Work
1
Air pollution in China: Status and spatiotemporal variations
Hit paper breakdown →
2017552
2
Self-Supervised Learning of Graph Neural Networks: A Unified Review
Hit paper breakdown →
2022234
3 202270
4 201656
5 202127
6 202218
7 202116
8 202413
9 201912
10 20067
11 20186
12 20083
13 20252
14
Noise2Same: Optimizing A Self-Supervised Bound for Image Denoising
20201
15 20051
16 20221
17 20201
18 20251
19 20240

About Yaochen Xie

Yaochen Xie is a scholar working on Artificial Intelligence, Information Systems, Media Technology, Computer Vision and Pattern Recognition and Biophysics, having authored 19 papers that have together received 1.0k indexed citations. Recurring topics across this work include Image Processing Techniques and Applications (4 papers), Advanced Graph Neural Networks (3 papers), Cell Image Analysis Techniques (3 papers), Topic Modeling (3 papers), Text and Document Classification Technologies (3 papers), Advanced MIMO Systems Optimization (2 papers), Atmospheric chemistry and aerosols (2 papers) and Advanced Fluorescence Microscopy Techniques (2 papers). The work is most often cited by research in Health, Toxicology and Mutagenesis (445 citations), Environmental Engineering (253 citations), Atmospheric Science (270 citations), Artificial Intelligence (224 citations) and Automotive Engineering (82 citations). Yaochen Xie has collaborated with scholars based in United States and China. Frequent co-authors include Shuiwang Ji, Zhengyang Wang, Xu Zhao, Jianjun He, Lin Wu, Hongjun Mao, Baoshuang Liu, Qili Dai, Ting Wang and Taosheng Jin. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Environmental Pollution, Bioinformatics, IEEE Transactions on Medical Imaging and IEEE Transactions on Wireless Communications.

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.

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