Daixin Wang
Impact in
- Statistical and Nonlinear Physics top 0.5%
- Complex Network Analysis Techniques
- Artificial Intelligence top 0.5%
- Advanced Graph Neural Networks
- Topic Modeling
- Domain Adaptation and Few-Shot Learning
Papers in
-
- Multimodal Machine Learning Applications 3
- Advanced Image and Video Retrieval Techniques 3
- Image Retrieval and Classification Techniques 2
-
- Advanced Graph Neural Networks 3
- Co-authors
- Peng Cui (7 shared papers)Wenwu Zhu (6 shared papers)Mingdong Ou (2 shared papers)Dingyuan Zhu (1 shared paper)Ke Tu (1 shared paper)Zhiqiang Zhang (1 shared paper)Qi Yuan (1 shared paper)Jun Zhou (1 shared paper)
- Journals
- IEEE Transactions on Multimedia (1 paper)Annals of Human Biology (1 paper)2020 IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC) (1 paper)Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (1 paper)International Conference on Artificial Intelligence (1 paper)
- Partner nations
- China
In The Last Decade
Daixin Wang
9 papers receiving 1.9k citations
Daixin Wang's Hit Papers
Peers
Comparison fields: 5 of 90
- Statistical and Nonlinear Physics 928
- Artificial Intelligence 1.5k
- Computer Vision and Pattern Recognition 375
- Information Systems 395
- Computational Mathematics 8
Countries citing papers authored by Daixin Wang
This map shows the geographic impact of Daixin Wang'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 Daixin Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daixin Wang more than expected).
Fields of papers citing papers by Daixin Wang
This network shows the impact of papers produced by Daixin Wang. 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 Daixin Wang. The network helps show where Daixin Wang may publish in the future.
Co-authors
The 20 scholars most cited alongside Daixin Wang, 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 | Structural Deep Network Embedding Hit paper breakdown → | 2016 | 1681 |
| 2 | 2018 | 80 | |
| 3 | 2015 | 75 | |
| 4 | Deep multimodal hashing with orthogonal regularization | 2015 | 74 |
| 5 | 2021 | 35 | |
| 6 | 2021 | 19 | |
| 7 | 2018 | 17 | |
| 8 | 2019 | 2 | |
| 9 | 2020 | 1 | |
| 10 | 2022 | 1 |
About Daixin Wang
Daixin Wang is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Information Systems, Statistical and Nonlinear Physics and Molecular Biology, having authored 10 papers that have together received 2.0k indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (3 papers), Advanced Graph Neural Networks (3 papers), Advanced Image and Video Retrieval Techniques (3 papers), Recommender Systems and Techniques (2 papers), Complex Network Analysis Techniques (2 papers), Image Retrieval and Classification Techniques (2 papers), Spam and Phishing Detection (1 paper) and RFID technology advancements (1 paper). The work is most often cited by research in Statistical and Nonlinear Physics (928 citations), Artificial Intelligence (1.5k citations), Computer Vision and Pattern Recognition (375 citations), Information Systems (395 citations) and Computational Mathematics (8 citations). Daixin Wang has collaborated with scholars based in China. Frequent co-authors include Peng Cui, Wenwu Zhu, Mingdong Ou, Dingyuan Zhu, Ke Tu, Zhiqiang Zhang, Qi Yuan, Jun Zhou, Jun Zhou and Zhiqiang Zhang. Their work appears in journals such as IEEE Transactions on Multimedia, Annals of Human Biology, 2020 IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC), Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining and International Conference on Artificial Intelligence.
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.