Deyu Bo
Impact in
- Artificial Intelligence top 5%
- Advanced Graph Neural Networks
- Topic Modeling
- Domain Adaptation and Few-Shot Learning
- Text and Document Classification Technologies
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- Complex Network Analysis Techniques
Papers in
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- Advanced Graph Neural Networks 6
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- Gene expression and cancer classification 1
- Co-authors
- Xiao Wang (3 shared papers)Chuan Shi (5 shared papers)Huawei Shen (1 shared paper)Shaohua Fan (1 shared paper)Philip S. Yu (1 shared paper)Yanfang Ye (1 shared paper)Zhiqiang Zhang (1 shared paper)Jun Zhou (1 shared paper)
- Journals
- IEEE Transactions on Big Data (2 papers)Singapore Management University Institutional Knowledge (InK) (Singapore Management University) (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (2 papers)
- Partner nations
- ChinaUnited StatesSingapore
In The Last Decade
Deyu Bo
5 papers receiving 565 citations
Deyu Bo's Hit Papers
Peers
Comparison fields: 5 of 65
- Artificial Intelligence 463
- Statistical and Nonlinear Physics 151
- Computer Vision and Pattern Recognition 146
- Information Systems 140
- Neurology 27
Countries citing papers authored by Deyu Bo
This map shows the geographic impact of Deyu Bo'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 Deyu Bo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Deyu Bo more than expected).
Fields of papers citing papers by Deyu Bo
This network shows the impact of papers produced by Deyu Bo. 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 Deyu Bo. The network helps show where Deyu Bo may publish in the future.
Co-authors
The 12 scholars most cited alongside Deyu Bo, 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 | Beyond Low-frequency Information in Graph Convolutional Networks Hit paper breakdown → | 2021 | 309 |
| 2 | A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources Hit paper breakdown → | 2022 | 237 |
| 3 | 2022 | 23 | |
| 4 | 2024 | 4 | |
| 5 | 2025 | 1 | |
| 6 | 2023 | 0 |
About Deyu Bo
Deyu Bo is a scholar working on Artificial Intelligence, Molecular Biology, Health Information Management, Information Systems and Computer Vision and Pattern Recognition, having authored 6 papers that have together received 574 indexed citations. Recurring topics across this work include Advanced Graph Neural Networks (6 papers), Graph Theory and Algorithms (1 paper), Artificial Intelligence in Healthcare (1 paper), Data Quality and Management (1 paper), EEG and Brain-Computer Interfaces (1 paper), Brain Tumor Detection and Classification (1 paper), Gene expression and cancer classification (1 paper) and Complex Network Analysis Techniques (1 paper). The work is most often cited by research in Artificial Intelligence (463 citations), Statistical and Nonlinear Physics (151 citations), Computer Vision and Pattern Recognition (146 citations), Information Systems (140 citations) and Neurology (27 citations). Deyu Bo has collaborated with scholars based in China, United States and Singapore. Frequent co-authors include Xiao Wang, Chuan Shi, Huawei Shen, Shaohua Fan, Philip S. Yu, Yanfang Ye, Zhiqiang Zhang, Jun Zhou, Binbin Hu and Cheng Yang. Their work appears in journals such as IEEE Transactions on Big Data, Singapore Management University Institutional Knowledge (InK) (Singapore Management University) and Proceedings of the AAAI 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.