Wei Ding

6.9k total citations · 1 hit paper
166 papers, 4.4k citations indexed

About

Wei Ding is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems. According to data from OpenAlex, Wei Ding has authored 166 papers receiving a total of 4.4k indexed citations (citations by other indexed papers that have themselves been cited), including 81 papers in Artificial Intelligence, 44 papers in Computer Vision and Pattern Recognition and 26 papers in Information Systems. Recurrent topics in Wei Ding's work include Data Mining Algorithms and Applications (18 papers), Topic Modeling (14 papers) and Face and Expression Recognition (14 papers). Wei Ding is often cited by papers focused on Data Mining Algorithms and Applications (18 papers), Topic Modeling (14 papers) and Face and Expression Recognition (14 papers). Wei Ding collaborates with scholars based in United States, China and Australia. Wei Ding's co-authors include Xindong Wu, Xingquan Zhu, Gongqing Wu, Kui Yu, T. F. Stepinski, Melissa S. Morabito, Jian Pei, Yang Mu, Hao Wang and Ping Chen and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Pattern Analysis and Machine Intelligence and British Journal of Cancer.

In The Last Decade

Wei Ding

155 papers receiving 4.2k 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.

Data mining with big data

2013 1.6k citations Xindong Wu, Wei Ding et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Wei Ding United States	31	2.2k	1.1k	989	429	413	166	4.4k
D. Sculley United States	21	1.7k 0.8×	940 0.9×	641 0.6×	261 0.6×	453 1.1×	37	3.4k
Zhongzhi Shi China	36	2.6k 1.2×	1.3k 1.3×	1.1k 1.1×	394 0.9×	632 1.5×	383	5.1k
Pádraig Cunningham Ireland	37	3.1k 1.4×	1.1k 1.1×	950 1.0×	518 1.2×	587 1.4×	205	5.9k
Xiaoyong Du China	29	1.9k 0.8×	1.1k 1.0×	692 0.7×	430 1.0×	824 2.0×	317	3.5k
Stan Matwin Canada	36	4.3k 1.9×	1.3k 1.3×	664 0.7×	607 1.4×	545 1.3×	263	6.8k
Joshua Zhexue Huang China	38	2.9k 1.3×	1.3k 1.2×	1.3k 1.3×	566 1.3×	1.2k 2.9×	195	5.5k
Chih‐Fong Tsai Taiwan	43	3.8k 1.7×	937 0.9×	850 0.9×	675 1.6×	922 2.2×	141	7.8k
Marco Túlio Ribeiro United States	13	6.4k 2.8×	804 0.8×	1.1k 1.1×	441 1.0×	369 0.9×	22	9.3k
Zhao Li China	36	4.3k 1.9×	1.3k 1.2×	1.1k 1.1×	575 1.3×	1.1k 2.6×	450	7.2k
Petra Perner Germany	19	2.0k 0.9×	1.6k 1.5×	670 0.7×	663 1.5×	416 1.0×	105	4.2k

Countries citing papers authored by Wei Ding

Since Specialization

Citations

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

Fields of papers citing papers by Wei Ding

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Wei Ding

This figure shows the co-authorship network connecting the top 25 collaborators of Wei Ding. A scholar is included among the top collaborators of Wei 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 Wei Ding. Wei Ding 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:

20 of 20 papers shown

1.

Zhao, Peipei, et al.. (2025). Learning multi-scale attention network for fine-grained visual classification. SHILAP Revista de lepidopterología. 3(6). 492–503.

2.

Ma, Shuaiyin, Wei Ding, Yujuan Zheng, et al.. (2024). Edge-cloud collaboration-driven predictive planning based on LSTM-attention for wastewater treatment. Computers & Industrial Engineering. 195. 110425–110425. 10 indexed citations

3.

Ding, Wei, et al.. (2024). Remote Sensing Thematic Product Generation for Sustainable Development of the Geological Environment. Remote Sensing. 16(14). 2529–2529.

4.

Jiang, Han-Peng, Zhennan Chen, Wei Ding, & Fan Lin. (2024). Asformer: Learning From Adjacent Scale. 5900–5904.

5.

Tian, Feng, et al.. (2023). New User Intent Discovery With Robust Pseudo Label Training and Source Domain Joint Training. IEEE Intelligent Systems. 38(4). 21–31. 6 indexed citations

6.

Ding, Wei, et al.. (2023). High-Resistance Connection Diagnosis of Doubly Fed Induction Generators. Energies. 16(22). 7516–7516. 1 indexed citations

7.

Ding, Wei, et al.. (2022). SummerTime: Variable-length Time Series Summarization with Application to Physical Activity Analysis. 3(4). 1–15. 1 indexed citations

8.

Ding, Wei, et al.. (2022). Smart Evaluation of Sustainability of Photovoltaic Projects in the Context of Carbon Neutrality Target. Sustainability. 14(22). 14925–14925. 1 indexed citations

9.

Zhuang, Yong, et al.. (2021). Mitigating Class-Boundary Label Uncertainty to Reduce Both Model Bias and Variance. ACM Transactions on Knowledge Discovery from Data. 15(2). 1–18. 7 indexed citations

10.

Qiang, Jipeng, Wei Ding, Marieke L. Kuijjer, John Quackenbush, & Ping Chen. (2020). Clustering Sparse Data With Feature Correlation With Application to Discover Subtypes in Cancer. IEEE Access. 8. 67775–67789. 1 indexed citations

11.

Li, Wei, Wei Ding, Rajani S. Sadasivam, Xiaohui Cui, & Ping Chen. (2019). His-GAN: A histogram-based GAN model to improve data generation quality. Neural Networks. 119. 31–45. 41 indexed citations

12.

Cohen, Joseph, Henry Z. Lo, Tingting Lu, & Wei Ding. (2016). Crater Detection via Convolutional Neural Networks. arXiv (Cornell University). 1143. 3 indexed citations

13.

Ma, Meng, Ru Ying, Ling-Shiang Chuang, et al.. (2015). Disease-associated variants in different categories of disease located in distinct regulatory elements. BMC Genomics. 16(S8). S3–S3. 40 indexed citations

14.

Jiang, He, Wei Ding, Moonis Ali, & Xindong Wu. (2012). Proceedings of the 25th international conference on Industrial Engineering and Other Applications of Applied Intelligent Systems: advanced research in applied artificial intelligence. 1 indexed citations

15.

Cohen, Joseph, et al.. (2012). Mars Weekend: A Panel and Games at the Museum of Science Boston. Lunar and Planetary Science Conference. 1023. 1 indexed citations

16.

Liu, Hong, et al.. (2011). Feasibility study for automatic calibration of transportation simulation models. Annual Simulation Symposium. 87–94. 3 indexed citations

17.

Liu, Siyi, et al.. (2011). Semi-supervised based active class selection for automatic identification of sub-kilometer craters. 615–620. 5 indexed citations

18.

Wu, Xindong, Kui Yu, Hao Wang, & Wei Ding. (2010). Online Streaming Feature Selection. International Conference on Machine Learning. 1159–1166. 88 indexed citations

19.

Ding, Wei, et al.. (2009). Word Classification: An Experimental Approach with Naïve Bayes.. Computers and Their Applications. 215–221. 2 indexed citations

20.

Chen, Ping, et al.. (2008). Parsing tree matching based question answering. Theory and applications of categories. 1 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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