Qing Cui

1.4k total citations · 1 hit paper
36 papers, 876 citations indexed

About

Qing Cui is a scholar working on Artificial Intelligence, Mechanical Engineering and Materials Chemistry. According to data from OpenAlex, Qing Cui has authored 36 papers receiving a total of 876 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Artificial Intelligence, 10 papers in Mechanical Engineering and 10 papers in Materials Chemistry. Recurrent topics in Qing Cui's work include Microstructure and Mechanical Properties of Steels (8 papers), Recommender Systems and Techniques (6 papers) and Metal Alloys Wear and Properties (5 papers). Qing Cui is often cited by papers focused on Microstructure and Mechanical Properties of Steels (8 papers), Recommender Systems and Techniques (6 papers) and Metal Alloys Wear and Properties (5 papers). Qing Cui collaborates with scholars based in China, United States and Mexico. Qing Cui's co-authors include Enhong Chen, Bin Gao, Tie‐Yan Liu, Fei Tian, Jun Zhou, Hao Qian, Chi Zhang, Xiaoqiang Li, Chenchong Wang and Wei Xu and has published in prestigious journals such as Materials Science and Engineering A, Expert Systems with Applications and IEEE Transactions on Cybernetics.

In The Last Decade

Qing Cui

31 papers receiving 855 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.

Learning Deep Representations for Graph Clustering

2014 428 citations Bin Gao, Qing Cui et al. Proceedings of the AAAI Conference on Artificial Intelligence profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Qing Cui China	12	492	208	196	144	82	36	876
Tiantian He China	16	261 0.5×	129 0.6×	89 0.5×	59 0.4×	17 0.2×	51	555
Sheng Gao China	17	648 1.3×	68 0.3×	225 1.1×	230 1.6×	68 0.8×	91	1.0k
Jianjia Wang China	17	355 0.7×	126 0.6×	147 0.8×	72 0.5×	26 0.3×	56	940
Stefanie Jegelka United States	18	657 1.3×	82 0.4×	488 2.5×	68 0.5×	255 3.1×	73	1.4k
Zhongyuan Zhang China	18	252 0.5×	308 1.5×	107 0.5×	55 0.4×	141 1.7×	103	1.1k
Xiangjun Li China	14	207 0.4×	90 0.4×	230 1.2×	82 0.6×	108 1.3×	51	751
Guangxia Li China	13	211 0.4×	20 0.1×	118 0.6×	93 0.6×	40 0.5×	63	578
Eric Zhao United States	4	890 1.8×	164 0.8×	232 1.2×	665 4.6×	28 0.3×	7	1.1k
Daniele Grattarola Switzerland	10	339 0.7×	86 0.4×	129 0.7×	61 0.4×	58 0.7×	15	591
Yuexin Wu China	14	797 1.6×	50 0.2×	262 1.3×	225 1.6×	35 0.4×	59	1.2k

Countries citing papers authored by Qing Cui

Since Specialization

Citations

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

Fields of papers citing papers by Qing Cui

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Qing Cui

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

All Works

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20 of 20 papers shown

1.

Sui, Yongduo, et al.. (2025). A Unified Invariant Learning Framework for Graph Classification. 1301–1312. 1 indexed citations

2.

Sui, Yongduo, Zhixuan Chu, Yuan Gao, et al.. (2024). Invariant Graph Learning for Causal Effect Estimation. 2552–2562. 3 indexed citations

3.

Sui, Yongduo, et al.. (2024). A Simple Data Augmentation for Graph Classification: A Perspective of Equivariance and Invariance. ACM Transactions on Knowledge Discovery from Data. 19(2). 1–24.

4.

Chu, Zhixuan, et al.. (2024). Task-Driven Causal Feature Distillation: Towards Trustworthy Risk Prediction. Proceedings of the AAAI Conference on Artificial Intelligence. 38(10). 11642–11650. 1 indexed citations

5.

Cui, Qing, et al.. (2024). Backdoor Adjustment via Group Adaptation for Debiased Coupon Recommendations. Proceedings of the AAAI Conference on Artificial Intelligence. 38(11). 11944–11952. 3 indexed citations

6.

Cui, Qing, et al.. (2024). Counterfactual Data Augmentation for Debiased Coupon Recommendations Based on Potential Knowledge. 93–102.

7.

Cui, Qing, et al.. (2023). GINT: A Generative Interpretability method via perturbation in the latent space. Expert Systems with Applications. 232. 120570–120570. 3 indexed citations

8.

Zhou, Jun, et al.. (2023). DistriBayes: A Distributed Platform for Learning, Inference and Attribution on Large Scale Bayesian Network. 1184–1187. 1 indexed citations

9.

Cui, Qing, et al.. (2023). High-pressure quenching effect on martensitic transformation characteristics and mechanical properties of low-alloy medium-carbon steel. Journal of Materials Research and Technology. 23. 765–777. 9 indexed citations

10.

Zhang, Kai, Qi Liu, Hao Qian, et al.. (2021). EATN: An Efficient Adaptive Transfer Network for Aspect-level Sentiment Analysis. IEEE Transactions on Knowledge and Data Engineering. 1–1. 78 indexed citations

11.

Wang, Chenchong, et al.. (2019). Design of Reduced Activation Ferritic/Martensitic Steels by Multiphase Optimization during the Entire Processing. ISIJ International. 59(9). 1715–1722. 9 indexed citations

12.

Pei, Changhua, Qing Cui, Lin Xiao, et al.. (2019). Value-aware Recommendation based on Reinforcement Profit Maximization. 3123–3129. 31 indexed citations

13.

Li, Xiaoqiang, Yin Zhang⋆, Qing Cui, Xiaoming Yi, & Yi Zhang. (2018). Tooth-Marked Tongue Recognition Using Multiple Instance Learning and CNN Features. IEEE Transactions on Cybernetics. 49(2). 380–387. 55 indexed citations

14.

Zhou, Jun, et al.. (2017). PSMART. 879–880. 7 indexed citations

15.

Li, Xiaoqiang, Dan Wang, & Qing Cui. (2017). WLDF: Effective Statistical Shape Feature for Cracked Tongue Recognition. Journal of Electrical Engineering and Technology. 12(1). 420–427. 3 indexed citations

16.

Zhou, Jun, Xiaolong Li, Peilin Zhao, et al.. (2017). KunPeng. 1693–1702. 27 indexed citations

17.

Cui, Qing, et al.. (2014). Co-learning of Word Representations and Morpheme Representations. International Conference on Computational Linguistics. 141–150. 44 indexed citations

18.

Cui, Qing, et al.. (2014). Learning Effective Word Embedding using Morphological Word Similarity.. arXiv (Cornell University). 5 indexed citations

19.

Yang, Li, Dong Wang, Lei Wang, et al.. (2011). Novel asymmetrical pyrene derivatives as light emitting materials: Synthesis and photophysics. Journal of Luminescence. 132(4). 1010–1014. 20 indexed citations

20.

Cui, Qing. (2009). IMPROVEMENT OF CARBIDE MORPHOLOGY AND MECHANICAL PROPERTIES OF Cr12MoV STEELS. Acta Metallurgica Sinica (English Letters). 13(5). 1053–1058.

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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