Xiangnan He
- Artificial Intelligence top 0.02%
- Information Systems top 0.01%
- Computer Vision and Pattern Recognition top 0.1%
- Management Science and Operations Research top 0.1%
- Computer Networks and Communications top 0.5%
- Topics
- Recommender Systems and Techniques (148 papers)Advanced Graph Neural Networks (97 papers)Topic Modeling (76 papers)
- Journals
- Proceedings of the National Academy of SciencesNature CommunicationsIEEE Transactions on Pattern Analysis and Machine Intelligence
- Partner nations
- ChinaSingaporeUnited States
In The Last Decade
Xiangnan He
258 papers receiving 17.0k citations
Hit Papers
Peers
Comparison fields: 5 of 170
- Artificial Intelligence 11.5k
- Information Systems 10.8k
- Computer Vision and Pattern Recognition 5.0k
- Management Science and Operations Research 2.1k
- Computer Networks and Communications 1.5k
Countries citing papers authored by Xiangnan He
This map shows the geographic impact of Xiangnan He'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 Xiangnan He with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xiangnan He more than expected).
Fields of papers citing papers by Xiangnan He
This network shows the impact of papers produced by Xiangnan He. 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 Xiangnan He. The network helps show where Xiangnan He may publish in the future.
Co-authorship network of co-authors of Xiangnan He
This figure shows the co-authorship network connecting the top 25 collaborators of Xiangnan He. A scholar is included among the top collaborators of Xiangnan He 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 Xiangnan He. Xiangnan He is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 0 | |
| 4 | 4 | |
| 5 | 26 | |
| 6 | 6 | |
| 7 | 0 | |
| 8 | 1 | |
| 9 | 0 | |
| 10 | 14 | |
| 11 | 12 | |
| 12 | 6 | |
| 13 | 15 | |
| 14 | 70 | |
| 15 | TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendationbreakdown → | 155 |
| 16 | 28 | |
| 17 | 60 | |
| 18 | 1 | |
| 19 | 131 | |
| 20 | 22 |
About Xiangnan He
Xiangnan He is a scholar working on Information Systems, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 275 papers that have together received 17.3k indexed citations. Recurring topics across this work include Recommender Systems and Techniques (148 papers), Advanced Graph Neural Networks (97 papers) and Topic Modeling (76 papers). The work is most often cited by research in Information Systems (10.8k citations), Artificial Intelligence (11.5k citations) and Computer Vision and Pattern Recognition (5.0k citations). Xiangnan He has collaborated with scholars based in China, Singapore and United States. Frequent co-authors include Tat‐Seng Chua, Xiang Wang, Hanwang Zhang, Yongdong Zhang, Meng Wang, Fuli Feng, Min‐Yen Kan, Yan Li, Liqiang Nie and Yong Li. Their work appears in journals such as Proceedings of the National Academy of Sciences, Nature Communications and IEEE Transactions on Pattern Analysis and Machine 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.