Xinyu Dai

4.7k total citations · 1 hit paper
142 papers, 2.7k citations indexed

About

Xinyu Dai is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Molecular Biology. According to data from OpenAlex, Xinyu Dai has authored 142 papers receiving a total of 2.7k indexed citations (citations by other indexed papers that have themselves been cited), including 91 papers in Artificial Intelligence, 23 papers in Computer Vision and Pattern Recognition and 18 papers in Molecular Biology. Recurrent topics in Xinyu Dai's work include Topic Modeling (66 papers), Natural Language Processing Techniques (50 papers) and Sentiment Analysis and Opinion Mining (18 papers). Xinyu Dai is often cited by papers focused on Topic Modeling (66 papers), Natural Language Processing Techniques (50 papers) and Sentiment Analysis and Opinion Mining (18 papers). Xinyu Dai collaborates with scholars based in China, United States and Hong Kong. Xinyu Dai's co-authors include Shujian Huang, Jiajun Chen, Jianbing Zhang, Jiajun Chen, Zhen Wu, Fei Zhao, Zhifang Fan, Rui Xia, Qiulin Tang and Hongwei Xia and has published in prestigious journals such as PLoS ONE, Analytical Chemistry and Scientific Reports.

In The Last Decade

Xinyu Dai

122 papers receiving 2.6k citations

Hit Papers

Deep Matrix Factorization Models for Recommender Systems 2017 2026 2020 2023 2017 100 200 300 400 500

Peers

Xinyu Dai
Comparison fields: 5 of 165
  • Artificial Intelligence 1.6k
  • Information Systems 721
  • Computer Vision and Pattern Recognition 455
  • Molecular Biology 316
  • Biomedical Engineering 145
Replace Yejin Kim with:
Yejin Kim South Korea
Quan Fang China
Yu Rong China
Lin Gui China
Liang Pang China
Zheng Chen China
Dilip Kumar Sharma India
Bingchen Li China
Richard M. Young United Kingdom
Qin Lu Hong Kong
Yejin Kim South Korea View profile →
Citations per field, relative to Xinyu Dai
Xinyu Dai · 1×
Citations per year, relative to Xinyu Dai
Xinyu Dai · 1×

Countries citing papers authored by Xinyu Dai

Since Specialization
Citations

This map shows the geographic impact of Xinyu Dai'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 Xinyu Dai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xinyu Dai more than expected).

Fields of papers citing papers by Xinyu Dai

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Xinyu Dai. 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 Xinyu Dai. The network helps show where Xinyu Dai may publish in the future.

Co-authorship network of co-authors of Xinyu Dai

This figure shows the co-authorship network connecting the top 25 collaborators of Xinyu Dai. A scholar is included among the top collaborators of Xinyu Dai 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 Xinyu Dai. Xinyu Dai is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
# Work Indexed citations
1 0
2 0
3 4
4 1
5 0
6 0
7 9
8 1
9 0
10 52
11 0
12 24
13 4
14 8
15 6
16 1
17 7
18 7
19 2
20 16

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.

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2026