Daixin Wang

3.3k total citations · 1 hit paper
10 papers, 2.0k citations indexed

About

Daixin Wang is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Information Systems. According to data from OpenAlex, Daixin Wang has authored 10 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Computer Vision and Pattern Recognition, 5 papers in Artificial Intelligence and 3 papers in Information Systems. Recurrent topics in Daixin Wang's work include Advanced Image and Video Retrieval Techniques (3 papers), Multimodal Machine Learning Applications (3 papers) and Advanced Graph Neural Networks (3 papers). Daixin Wang is often cited by papers focused on Advanced Image and Video Retrieval Techniques (3 papers), Multimodal Machine Learning Applications (3 papers) and Advanced Graph Neural Networks (3 papers). Daixin Wang collaborates with scholars based in China. Daixin Wang's co-authors include Wenwu Zhu, Peng Cui, Mingdong Ou, Dingyuan Zhu, Qi Yuan, Ke Tu, Jun Zhou, Zhiqiang Zhang, Qing He and Zhiqiang Zhang and has published in prestigious journals such as IEEE Transactions on Multimedia, Annals of Human Biology and Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.

In The Last Decade

Daixin Wang

9 papers receiving 1.9k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Structural Deep Network Embedding

2016 1.7k citations Daixin Wang, Peng Cui et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Daixin Wang China	7	1.5k	922	391	374	370	10	2.0k
Shaosheng Cao China	7	1.4k 1.0×	923 1.0×	304 0.8×	198 0.5×	368 1.0×	20	1.8k
Qiongkai Xu Australia	10	1.5k 1.0×	924 1.0×	343 0.9×	196 0.5×	364 1.0×	30	1.8k
Palash Goyal United States	8	1.1k 0.7×	614 0.7×	329 0.8×	226 0.6×	251 0.7×	30	1.6k
Hongyun Cai Singapore	6	1.1k 0.8×	620 0.7×	294 0.8×	250 0.7×	234 0.6×	7	1.5k
Galileo Namata United States	11	2.0k 1.3×	908 1.0×	393 1.0×	460 1.2×	186 0.5×	18	2.3k
Dongxiao He China	26	1.3k 0.9×	1.3k 1.4×	314 0.8×	276 0.7×	191 0.5×	105	2.0k
Mingdong Ou China	7	680 0.5×	504 0.5×	197 0.5×	278 0.7×	192 0.5×	9	1.0k
Nagarajan Natarajan United States	19	683 0.5×	236 0.3×	268 0.7×	209 0.6×	298 0.8×	63	1.6k
Glen Jeh United States	7	1.3k 0.9×	1.0k 1.1×	902 2.3×	404 1.1×	236 0.6×	7	2.3k
Kazumi Saito Japan	18	582 0.4×	705 0.8×	261 0.7×	286 0.8×	89 0.2×	102	1.5k

Countries citing papers authored by Daixin Wang

Since Specialization

Citations

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

Fields of papers citing papers by Daixin Wang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daixin Wang

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

All Works

Sort: Min cites: Since: Top N: Style:

10 of 10 papers shown

1.

Wang, Daixin, Zhengwei Wu, Zhiqiang Zhang, et al.. (2022). A Graph Learning Based Framework for Billion-Scale Offline User Identification. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2. 4001–4009. 1 indexed citations

2.

Zhuang, Fuzhen, et al.. (2021). Low-dimensional Alignment for Cross-Domain Recommendation. 3508–3512. 17 indexed citations

3.

Tu, Ke, Peng Cui, Daixin Wang, et al.. (2021). Conditional Graph Attention Networks for Distilling and Refining Knowledge Graphs in Recommendation. 1834–1843. 34 indexed citations

4.

Wang, Daixin, et al.. (2020). Design and Optimization of DD Coupling Mechanism of Inductively Coupled Power Transmission System in Metal Environment. 2020 IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC). 206–210. 1 indexed citations

5.

Xu, Wei, et al.. (2019). Mutation analysis of 21 autosomal short tandem repeats in Han population from Hunan, China. Annals of Human Biology. 46(3). 254–260. 2 indexed citations

6.

Zhu, Dingyuan, Peng Cui, Daixin Wang, & Wenwu Zhu. (2018). Deep Variational Network Embedding in Wasserstein Space. 2827–2836. 78 indexed citations

7.

Wang, Daixin, Peng Cui, & Wenwu Zhu. (2018). Deep Asymmetric Transfer Network for Unbalanced Domain Adaptation. Proceedings of the AAAI Conference on Artificial Intelligence. 32(1). 16 indexed citations

8.

Wang, Daixin, Peng Cui, & Wenwu Zhu. (2016). Structural Deep Network Embedding. 1225–1234. 1662 indexed citations breakdown →

9.

Wang, Daixin, Peng Cui, Mingdong Ou, & Wenwu Zhu. (2015). Deep multimodal hashing with orthogonal regularization. International Conference on Artificial Intelligence. 2291–2297. 74 indexed citations

10.

Wang, Daixin, Peng Cui, Mingdong Ou, & Wenwu Zhu. (2015). Learning Compact Hash Codes for Multimodal Representations Using Orthogonal Deep Structure. IEEE Transactions on Multimedia. 17(9). 1404–1416. 75 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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