Yongduo Sui
- Artificial Intelligence top 10%
- Advanced Graph Neural Networks 13
- Explainable Artificial Intelligence (XAI) 3
- Topic Modeling 3
- Bayesian Modeling and Causal Inference 2
- Domain Adaptation and Few-Shot Learning 2
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- Recommender Systems and Techniques 3
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- Visual Attention and Saliency Detection 2
- Graph Theory and Algorithms 2
- Co-authors
- Xiang WangJiancan WuXiangnan HeTat‐Seng ChuaTianlong ChenZhangyang WangYuning YouTing Chen
- Journals
- International Journal of Intelligent Systems (1 paper)ACM Transactions on Knowledge Discovery from Data (2 papers)Frontiers of Computer Science (1 paper)
- Partner nations
- ChinaSingaporeUnited States
In The Last Decade
Yongduo Sui
16 papers receiving 224 citations
Peers
Comparison fields: 5 of 44
- Artificial Intelligence 187
- Information Systems 44
- Computer Vision and Pattern Recognition 39
- Signal Processing 14
- Statistical and Nonlinear Physics 15
Countries citing papers authored by Yongduo Sui
This map shows the geographic impact of Yongduo Sui'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 Yongduo Sui with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yongduo Sui more than expected).
Fields of papers citing papers by Yongduo Sui
This network shows the impact of papers produced by Yongduo Sui. 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 Yongduo Sui. The network helps show where Yongduo Sui may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Yongduo Sui, 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 | 2025 | 1 | |
| 2 | 2025 | 1 | |
| 3 | 2025 | 1 | |
| 4 | 2024 | 4 | |
| 5 | 2024 | 3 | |
| 6 | 2024 | 0 | |
| 7 | 2024 | 10 | |
| 8 | 2024 | 14 | |
| 9 | 2024 | 12 | |
| 10 | 2024 | 4 | |
| 11 | 2024 | 3 | |
| 12 | 2023 | 20 | |
| 13 | 2022 | 95 | |
| 14 | 2022 | 6 | |
| 15 | 2022 | 6 | |
| 16 | 2021 | 2 | |
| 17 | Graph Contrastive Learning with Augmentations | 2020 | 46 |
About Yongduo Sui
Yongduo Sui is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Human-Computer Interaction, having authored 17 papers that have together received 228 indexed citations. Recurring topics across this work include Advanced Graph Neural Networks (13 papers), Explainable Artificial Intelligence (XAI) (3 papers), Topic Modeling (3 papers), Recommender Systems and Techniques (3 papers), Visual Attention and Saliency Detection (2 papers), Graph Theory and Algorithms (2 papers), Bayesian Modeling and Causal Inference (2 papers) and Domain Adaptation and Few-Shot Learning (2 papers). The work is most often cited by research in Artificial Intelligence (187 citations), Information Systems (44 citations) and Computer Vision and Pattern Recognition (39 citations). Yongduo Sui has collaborated with scholars based in China, Singapore and United States. Frequent co-authors include Xiang Wang, Jiancan Wu, Xiangnan He, Tat‐Seng Chua, Tianlong Chen, Zhangyang Wang, Yuning You, Ting Chen, Yang Shen and Chao Wang. Their work appears in journals such as International Journal of Intelligent Systems, ACM Transactions on Knowledge Discovery from Data and Frontiers of Computer Science.
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.