Qingqiang Sun

1.1k total citations · 1 hit paper
17 papers, 765 citations indexed

About

Qingqiang Sun is a scholar working on Artificial Intelligence, Control and Systems Engineering and Biomedical Engineering. According to data from OpenAlex, Qingqiang Sun has authored 17 papers receiving a total of 765 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 6 papers in Control and Systems Engineering and 3 papers in Biomedical Engineering. Recurrent topics in Qingqiang Sun's work include Fault Detection and Control Systems (6 papers), Advanced Graph Neural Networks (4 papers) and Fusion materials and technologies (2 papers). Qingqiang Sun is often cited by papers focused on Fault Detection and Control Systems (6 papers), Advanced Graph Neural Networks (4 papers) and Fusion materials and technologies (2 papers). Qingqiang Sun collaborates with scholars based in China, Australia and Hong Kong. Qingqiang Sun's co-authors include Zhiqiang Ge, Zhiqiang Ge, Kai Wang, Xiaofeng Yuan, Yalin Wang, Xinggui Long, Xuemin Lin, Wenjie Zhang, Zhaoxia Hu and Ying Zhang and has published in prestigious journals such as Advanced Functional Materials, Journal of Membrane Science and IEEE Transactions on Cybernetics.

In The Last Decade

Qingqiang Sun

16 papers receiving 750 citations

Hit Papers

A Survey on Deep Learning for Data-Driven Soft Sensors 2021 2026 2022 2024 2021 100 200 300
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.

  1. A Survey on Deep Learning for Data-Driven Soft Sensors
    2021 393 citations Qingqiang Sun, Zhiqiang Ge IEEE Transactions on Industrial Informatics profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Qingqiang Sun China 7 502 285 213 97 58 17 765
Lingjian Ye China 20 867 1.7× 260 0.9× 288 1.4× 86 0.9× 90 1.6× 103 1.2k
Huaiping Jin China 19 546 1.1× 237 0.8× 174 0.8× 273 2.8× 111 1.9× 56 1.0k
Xiangguang Chen China 15 449 0.9× 182 0.6× 173 0.8× 33 0.3× 92 1.6× 57 795
Shiwen Xie China 18 331 0.7× 219 0.8× 172 0.8× 83 0.9× 13 0.2× 53 703
Zhizhong Mao China 17 334 0.7× 459 1.6× 251 1.2× 38 0.4× 58 1.0× 58 853
Xuejin Gao China 13 365 0.7× 92 0.3× 199 0.9× 55 0.6× 54 0.9× 76 503
Zeyu Yang China 13 203 0.4× 126 0.4× 98 0.5× 42 0.4× 33 0.6× 58 508

Countries citing papers authored by Qingqiang Sun

Since Specialization
Citations

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

Fields of papers citing papers by Qingqiang Sun

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Qingqiang Sun

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

All Works

17 of 17 papers shown
1.
Liu, Mingxu, et al.. (2025). Perfluorocarbon Chain‐Ti 3 C 2 T x MXene for Triboelectric Sensor. Advanced Functional Materials. 36(23).
2.
Sun, Qingqiang, Chaoqi Chen, Ziyue Qiao, Xubin Zheng, & Kai Wang. (2025). Single-View Graph Contrastive Learning with Soft Neighborhood Awareness. Proceedings of the AAAI Conference on Artificial Intelligence. 39(19). 20708–20716. 1 indexed citations
3.
Liu, Yanran, et al.. (2025). PRAGA: Prototype-aware Graph Adaptive Aggregation for Spatial Multi-modal Omics Analysis. Proceedings of the AAAI Conference on Artificial Intelligence. 39(1). 326–333. 1 indexed citations
4.
Wang, Kai, et al.. (2024). A novel network for semantic segmentation of landslide areas in remote sensing images with multi-branch and multi-scale fusion. Applied Soft Computing. 158. 111542–111542. 9 indexed citations
5.
Sun, Qingqiang, Kai Wang, Wenjie Zhang, Peng Cheng, & Xuemin Lin. (2024). Interdependence-Adaptive Mutual Information Maximization for Graph Contrastive Learning. IEEE Transactions on Knowledge and Data Engineering. 36(12). 8556–8567. 2 indexed citations
6.
Sun, Qingqiang, Xuemin Lin, Ying Zhang, Wenjie Zhang, & Chaoqi Chen. (2023). Towards Higher-order Topological Consistency for Unsupervised Network Alignment. 177–190. 5 indexed citations
7.
8.
Sun, Qingqiang, Wenjie Zhang, & Xuemin Lin. (2023). Progressive Hard Negative Masking: From Global Uniformity to Local Tolerance. IEEE Transactions on Knowledge and Data Engineering. 35(12). 12932–12943. 2 indexed citations
9.
Wang, Kai, et al.. (2023). Manifold-constrained trace ratio optimization for nonstationary process performance monitoring. Journal of Process Control. 129. 103058–103058. 4 indexed citations
10.
Zheng, Junhua, Chao Wu, Qingqiang Sun, Zhihuan Song, & Le Zhou. (2022). Deep learning of complex process data for fault classification based on sparse probabilistic dynamic network. Journal of the Taiwan Institute of Chemical Engineers. 138. 104498–104498. 6 indexed citations
11.
Sun, Qingqiang & Zhiqiang Ge. (2021). A Survey on Deep Learning for Data-Driven Soft Sensors. IEEE Transactions on Industrial Informatics. 17(9). 5853–5866. 393 indexed citations breakdown →
12.
Sun, Qingqiang & Zhiqiang Ge. (2020). Gated Stacked Target-Related Autoencoder: A Novel Deep Feature Extraction and Layerwise Ensemble Method for Industrial Soft Sensor Application. IEEE Transactions on Cybernetics. 52(5). 3457–3468. 114 indexed citations
13.
Sun, Qingqiang & Zhiqiang Ge. (2020). Deep Learning for Industrial KPI Prediction: When Ensemble Learning Meets Semi-Supervised Data. IEEE Transactions on Industrial Informatics. 17(1). 260–269. 107 indexed citations
14.
Sun, Qingqiang & Zhiqiang Ge. (2018). Probabilistic Sequential Network for Deep Learning of Complex Process Data and Soft Sensor Application. IEEE Transactions on Industrial Informatics. 15(5). 2700–2709. 95 indexed citations
15.
Tang, Jing, et al.. (2017). DFT study of hydrogen and helium defects at the (1121) twin boundary in hcp scandium. International Journal of Modern Physics B. 31(11). 1750080–1750080. 2 indexed citations
16.
Sun, Qingqiang, Shuming Peng, Xinggui Long, et al.. (2016). First-principles study on the adsorption and dissociation of H2 molecules on Be(0 0 0 1) surfaces. Computational Materials Science. 117. 251–258. 6 indexed citations
17.
Yang, Li, et al.. (2013). First-principles study on elastic properties of hexagonal phase ErAx (A=H, He). Acta Physica Sinica. 62(11). 116201–116201. 7 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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