Tao Qi
- Artificial Intelligence top 1%
- Information Systems top 0.5%
- Computer Vision and Pattern Recognition top 5%
- Computer Networks and Communications top 10%
- Sociology and Political Science
- Topics
- Topic Modeling (28 papers)Recommender Systems and Techniques (26 papers)Advanced Graph Neural Networks (12 papers)
- Partner nations
- ChinaUnited KingdomJapan
In The Last Decade
Tao Qi
48 papers receiving 1.4k citations
Hit Papers
Peers
Comparison fields: 5 of 89
- Artificial Intelligence 1.1k
- Information Systems 970
- Computer Vision and Pattern Recognition 234
- Computer Networks and Communications 107
- Sociology and Political Science 104
Countries citing papers authored by Tao Qi
This map shows the geographic impact of Tao Qi'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 Tao Qi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tao Qi more than expected).
Fields of papers citing papers by Tao Qi
This network shows the impact of papers produced by Tao Qi. 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 Tao Qi. The network helps show where Tao Qi may publish in the future.
Co-authorship network of co-authors of Tao Qi
This figure shows the co-authorship network connecting the top 25 collaborators of Tao Qi. A scholar is included among the top collaborators of Tao Qi based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Tao Qi. Tao Qi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 1 | |
| 3 | 2 | |
| 4 | 6 | |
| 5 | 7 | |
| 6 | A federated graph neural network framework for privacy-preserving personalizationbreakdown → | 139 |
| 7 | Fastformer: Additive Attention is All You Need | 1 |
| 8 | 19 | |
| 9 | 85 | |
| 10 | 21 | |
| 11 | Privacy-Preserving News Recommendation Model Training via Federated Learning. | 6 |
| 12 | 23 | |
| 13 | FedRec: Privacy-Preserving News Recommendation with Federated Learning | 8 |
| 14 | MIND: A Large-scale Dataset for News Recommendationbreakdown → | 238 |
| 15 | 2 | |
| 16 | 30 | |
| 17 | 188 | |
| 18 | 10 | |
| 19 | Analog Circuit Fault Prediction Based on LS-SVM Optimized by PSO | 1 |
| 20 | Application of fractal theory to software complexity | 1 |
About Tao Qi
Tao Qi is a scholar working on Information Systems, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 49 papers that have together received 1.4k indexed citations. Recurring topics across this work include Topic Modeling (28 papers), Recommender Systems and Techniques (26 papers) and Advanced Graph Neural Networks (12 papers). The work is most often cited by research in Information Systems (970 citations), Artificial Intelligence (1.1k citations) and Computer Vision and Pattern Recognition (234 citations). Tao Qi has collaborated with scholars based in China, United Kingdom and Japan. Frequent co-authors include Chuhan Wu, Fangzhao Wu, Yongfeng Huang, Xing Xie, Suyu Ge, Jianxun Lian, Lingjuan Lyu, Ming Zhou, Jianfeng Gao and Danyang Liu. Their work appears in journals such as Nature Communications, Neurocomputing and Computer Networks.
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.