Daqing Wu

416 total citations
13 papers, 222 citations indexed

About

Daqing Wu is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Information Systems. According to data from OpenAlex, Daqing Wu has authored 13 papers receiving a total of 222 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Computer Vision and Pattern Recognition, 6 papers in Artificial Intelligence and 4 papers in Information Systems. Recurrent topics in Daqing Wu's work include Advanced Image and Video Retrieval Techniques (5 papers), Advanced Graph Neural Networks (5 papers) and Video Surveillance and Tracking Methods (4 papers). Daqing Wu is often cited by papers focused on Advanced Image and Video Retrieval Techniques (5 papers), Advanced Graph Neural Networks (5 papers) and Video Surveillance and Tracking Methods (4 papers). Daqing Wu collaborates with scholars based in China, United States and Singapore. Daqing Wu's co-authors include Xiao Luo, Chong Chen, Minghua Deng, Xian‐Sheng Hua, Jianqiang Huang, Haixin Wang, D. Erik Everhart, Zeyu Ma, Jinwen Ma and Yifang Qin and has published in prestigious journals such as International Journal of Psychophysiology, ACM Transactions on Multimedia Computing Communications and Applications and ACM Transactions on Knowledge Discovery from Data.

In The Last Decade

Daqing Wu

12 papers receiving 214 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Daqing Wu China 7 146 76 23 21 10 13 222
Yin-Hsi Kuo Taiwan 9 302 2.1× 42 0.6× 12 0.5× 11 0.5× 2 0.2× 27 326
Otilia Stretcu United States 6 98 0.7× 198 2.6× 23 1.0× 24 1.1× 5 0.5× 9 288
Christos Tzelepis United Kingdom 9 162 1.1× 65 0.9× 7 0.3× 8 0.4× 5 0.5× 21 208
Xianjing Han China 8 217 1.5× 77 1.0× 35 1.5× 22 1.0× 1 0.1× 10 278
Zhun Li China 4 187 1.3× 55 0.7× 61 2.7× 33 1.6× 13 1.3× 8 272
Tommaso Furlanello United States 3 82 0.6× 99 1.3× 5 0.2× 20 1.0× 4 0.4× 4 155
Yiling Wu China 11 331 2.3× 142 1.9× 11 0.5× 52 2.5× 4 0.4× 15 395
Balázs Kovács United States 3 192 1.3× 33 0.4× 32 1.4× 22 1.0× 1 0.1× 3 233
Sergios Petridis Greece 9 67 0.5× 66 0.9× 15 0.7× 6 0.3× 2 0.2× 18 156
Paul Mineiro United States 6 34 0.2× 72 0.9× 29 1.3× 30 1.4× 8 0.8× 16 145

Countries citing papers authored by Daqing Wu

Since Specialization
Citations

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

Fields of papers citing papers by Daqing Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daqing Wu

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

All Works

13 of 13 papers shown
1.
Luo, Xiao, Wei Ju, Yiyang Gu, et al.. (2023). Toward Effective Semi-supervised Node Classification with Hybrid Curriculum Pseudo-labeling. ACM Transactions on Multimedia Computing Communications and Applications. 20(3). 1–19. 13 indexed citations
3.
Wang, Pengfei, Daqing Wu, Chong Chen, et al.. (2023). Deep Adaptive Graph Clustering via von Mises-Fisher Distributions. ACM Transactions on the Web. 18(2). 1–21. 5 indexed citations
4.
Luo, Xiao, Daqing Wu, Yiyang Gu, et al.. (2023). Criterion-based Heterogeneous Collaborative Filtering for Multi-behavior Implicit Recommendation. ACM Transactions on Knowledge Discovery from Data. 18(1). 1–26. 4 indexed citations
5.
Wu, Daqing, et al.. (2022). Adaptive Harmony Learning and Optimization for Attributed Graph Clustering. 2022 International Joint Conference on Neural Networks (IJCNN). 1–8. 4 indexed citations
6.
Luo, Xiao, Haixin Wang, Daqing Wu, et al.. (2022). A Survey on Deep Hashing Methods. ACM Transactions on Knowledge Discovery from Data. 17(1). 1–50. 103 indexed citations
7.
Wu, Daqing, Xiao Luo, Zeyu Ma, et al.. (2021). Composition-Enhanced Graph Collaborative Filtering for Multi-behavior Recommendation. 1427–1432. 4 indexed citations
8.
Luo, Xiao, Daqing Wu, Chong Chen, Jinwen Ma, & Minghua Deng. (2021). Deep Unsupervised Hashing by Global and Local Consistency. 1–6. 12 indexed citations
9.
Luo, Xiao, Zeyu Ma, Daqing Wu, et al.. (2021). Deep Unsupervised Hashing by Distilled Smooth Guidance. 2 indexed citations
10.
Wu, Daqing, Xiao Luo, Zeyu Ma, et al.. (2021). ARGO: Modeling Heterogeneity in E-commerce Recommendation. arXiv (Cornell University). 1–8. 6 indexed citations
11.
Luo, Xiao, Daqing Wu, Zeyu Ma, et al.. (2021). CIMON: Towards High-quality Hash Codes. 902–908. 22 indexed citations
12.
Luo, Xiao, Daqing Wu, Zeyu Ma, et al.. (2021). A Statistical Approach to Mining Semantic Similarity for Deep Unsupervised Hashing. 4306–4314. 19 indexed citations
13.
Wu, Daqing, et al.. (2010). Brain function with complex decision making using electroencephalography. International Journal of Psychophysiology. 79(2). 175–183. 28 indexed citations

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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