Dawei Yin
- Artificial Intelligence top 0.5%
- Information Systems top 0.2%
- Computer Vision and Pattern Recognition top 2%
- Computer Networks and Communications top 5%
- Statistical and Nonlinear Physics top 2%
- Co-authors
- Qing LiWenqi FanJiliang TangYao MaYuan HeEric ZhaoLianghao XiaChao Huang
- Topics
- Topic Modeling (30 papers)Recommender Systems and Techniques (29 papers)Advanced Graph Neural Networks (20 papers)
- Partner nations
- ChinaHong KongUnited States
In The Last Decade
Dawei Yin
60 papers receiving 2.6k citations
Hit Papers
Peers
Comparison fields: 5 of 112
- Artificial Intelligence 2.0k
- Information Systems 1.6k
- Computer Vision and Pattern Recognition 493
- Computer Networks and Communications 309
- Statistical and Nonlinear Physics 281
Countries citing papers authored by Dawei Yin
This map shows the geographic impact of Dawei Yin'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 Dawei Yin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dawei Yin more than expected).
Fields of papers citing papers by Dawei Yin
This network shows the impact of papers produced by Dawei Yin. 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 Dawei Yin. The network helps show where Dawei Yin may publish in the future.
Co-authorship network of co-authors of Dawei Yin
This figure shows the co-authorship network connecting the top 25 collaborators of Dawei Yin. A scholar is included among the top collaborators of Dawei Yin 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 Dawei Yin. Dawei Yin 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 | 1 | |
| 3 | 6 | |
| 4 | 2 | |
| 5 | 11 | |
| 6 | 1 | |
| 7 | 0 | |
| 8 | 5 | |
| 9 | A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Modelsbreakdown → | 137 |
| 10 | 19 | |
| 11 | LLMRec: Large Language Models with Graph Augmentation for Recommendationbreakdown → | 87 |
| 12 | GraphGPT: Graph Instruction Tuning for Large Language Modelsbreakdown → | 57 |
| 13 | 1 | |
| 14 | Representation Learning with Large Language Models for Recommendationbreakdown → | 69 |
| 15 | 2 | |
| 16 | 4 | |
| 17 | 3 | |
| 18 | 8 | |
| 19 | 3 | |
| 20 | 142 |
About Dawei Yin
Dawei Yin is a scholar working on Information Systems, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 66 papers that have together received 2.7k indexed citations. Recurring topics across this work include Topic Modeling (30 papers), Recommender Systems and Techniques (29 papers) and Advanced Graph Neural Networks (20 papers). The work is most often cited by research in Information Systems (1.6k citations), Artificial Intelligence (2.0k citations) and Computer Vision and Pattern Recognition (493 citations). Dawei Yin has collaborated with scholars based in China, Hong Kong and United States. Frequent co-authors include Qing Li, Wenqi Fan, Jiliang Tang, Yao Ma, Yuan He, Eric Zhao, Lianghao Xia, Chao Huang, Jiashu Zhao and Yong Xu. Their work appears in journals such as IEEE Transactions on Knowledge and Data Engineering, Neural Networks and Machine Learning.
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.