Yue Ding

445 total citations
51 papers, 213 citations indexed

About

Yue Ding is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems. According to data from OpenAlex, Yue Ding has authored 51 papers receiving a total of 213 indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Artificial Intelligence, 17 papers in Computer Vision and Pattern Recognition and 15 papers in Information Systems. Recurrent topics in Yue Ding's work include Recommender Systems and Techniques (12 papers), Domain Adaptation and Few-Shot Learning (8 papers) and Advanced Graph Neural Networks (8 papers). Yue Ding is often cited by papers focused on Recommender Systems and Techniques (12 papers), Domain Adaptation and Few-Shot Learning (8 papers) and Advanced Graph Neural Networks (8 papers). Yue Ding collaborates with scholars based in China, United Kingdom and Jordan. Yue Ding's co-authors include Xin Xin, Hongtao Lu, Bo Chen, Dong Wang, Yuxiang Lu, Dong Wang, Xiangnan He, Joemon M. Jose, Xiuqiang He and Ruiming Tang and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Pattern Analysis and Machine Intelligence and Hepatology.

In The Last Decade

Yue Ding

40 papers receiving 210 citations

Peers

Yue Ding
Mohamed Elaraby Egypt
Muhammad Azeem China
Anand Shanker Tewari India
Mohamed Taha Egypt
Yangyang Wu China
Ambeshwar Kumar India
Anam Mustaqeem Pakistan
S. Sivasubramanian India
Mehmood Ali Mohammed United States
Mohamed Elaraby Egypt
Yue Ding
Citations per year, relative to Yue Ding Yue Ding (= 1×) peers Mohamed Elaraby

Countries citing papers authored by Yue Ding

Since Specialization
Citations

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

Fields of papers citing papers by Yue Ding

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yue Ding

This figure shows the co-authorship network connecting the top 25 collaborators of Yue Ding. A scholar is included among the top collaborators of Yue Ding 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 Yue Ding. Yue Ding 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
1.
Ding, Yue, et al.. (2025). Nonsingular fast predefined time convergence sliding mode control for construction robot. ISA Transactions. 161. 109–121. 2 indexed citations
2.
Ding, Yue, Xin Xin, Yuxiang Lu, et al.. (2025). Towards Personalized Federated Multi-Scenario Multi-Task Recommendation. 429–438. 1 indexed citations
3.
Liu, Yun, et al.. (2025). Timosaponin AⅢ inhibits ectopic lipid deposition and enhances the browning of white adipose tissue. European Journal of Pharmacology. 998. 177506–177506.
4.
Wu, Zhize, et al.. (2024). Local and global self-attention enhanced graph convolutional network for skeleton-based action recognition. Pattern Recognition. 159. 111106–111106. 15 indexed citations
5.
Li, Jie, et al.. (2024). Understanding and mitigating dimensional collapse of Graph Contrastive Learning: A non-maximum removal approach. Neural Networks. 181. 106652–106652. 1 indexed citations
6.
Lu, Yuxiang, et al.. (2024). TFUT: Task fusion upward transformer model for multi-task learning on dense prediction. Computer Vision and Image Understanding. 244. 104014–104014.
7.
Ding, Yue, et al.. (2024). DAG: Deep Adaptive and Generative K -Free Community Detection on Attributed Graphs. 5454–5465. 3 indexed citations
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10.
Lu, Yuxiang, et al.. (2024). Adaptive Task-Wise Message Passing for Multi-Task Learning: A Spatial Interaction Perspective. IEEE Transactions on Circuits and Systems for Video Technology. 34(10). 9499–9514. 1 indexed citations
11.
Bian, Jianjun, et al.. (2024). Influence of planar defects on the mechanical behaviors of spherical metallic nanoparticles. Physica Scripta. 100(1). 15921–15921. 3 indexed citations
12.
Luo, Wenqiang, Jionglin Wu, Qi Zhang, et al.. (2024). Evaluation of fragility fracture risk using deep learning based on ultrasound radio frequency signal. Endocrine. 86(2). 800–812. 1 indexed citations
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14.
Lu, Yuxiang, et al.. (2024). Task Indicating Transformer for Task-Conditional Dense Predictions. 35. 3625–3629. 1 indexed citations
15.
Zhao, Wenjun, et al.. (2024). Zhimu-Huangbai herb-pair ameliorates hepatic steatosis in mice by regulating IRE1α/XBP1s pathway to inhibit SREBP-1c. Phytomedicine. 134. 156017–156017. 3 indexed citations
16.
Liang, Dong, et al.. (2023). Spammer detection on short video applications. Pattern Recognition Letters. 177. 61–68. 2 indexed citations
17.
Lu, Yuxiang, et al.. (2023). Scale-Aware Task Message Transferring for Multi-Task Learning. 31. 1859–1864. 6 indexed citations
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19.
Chen, Bo, Huifeng Guo, Weiwen Liu, et al.. (2022). Numerical Feature Representation with HybridN-ary Encoding. Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 2984–2993.
20.
Liu, Jie, Dong Wang, & Yue Ding. (2017). PHD: A Probabilistic Model of Hybrid Deep Collaborative Filtering for Recommender Systems. Asian Conference on Machine Learning. 224–239. 12 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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