Ganqu Cui

7.3k total citations · 2 hit papers
20 papers, 4.2k citations indexed

About

Ganqu Cui is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Statistical and Nonlinear Physics. According to data from OpenAlex, Ganqu Cui has authored 20 papers receiving a total of 4.2k indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Artificial Intelligence, 5 papers in Computer Vision and Pattern Recognition and 3 papers in Statistical and Nonlinear Physics. Recurrent topics in Ganqu Cui's work include Topic Modeling (10 papers), Natural Language Processing Techniques (5 papers) and Speech Recognition and Synthesis (3 papers). Ganqu Cui is often cited by papers focused on Topic Modeling (10 papers), Natural Language Processing Techniques (5 papers) and Speech Recognition and Synthesis (3 papers). Ganqu Cui collaborates with scholars based in China, United States and Singapore. Ganqu Cui's co-authors include Zhiyuan Liu, Maosong Sun, Cheng Yang, Jie Zhou, Changcheng Li, Zhengyan Zhang, Shengding Hu, Lifeng Wang, Lifeng Wang and Jian Tang and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Neural Networks and Learning Systems and Transactions of the Association for Computational Linguistics.

In The Last Decade

Ganqu Cui

17 papers receiving 4.0k 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.

Graph neural networks: A review of methods and applications

2020 3.2k citations Jie Zhou, Ganqu Cui et al. SHILAP Revista de lepidopterología profile →
Graph Neural Networks: A Review of Methods and Applications

2018 767 citations Jie Zhou, Ganqu Cui et al. arXiv (Cornell University) profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Ganqu Cui China	8	2.0k	677	532	496	465	20	4.2k
Shengding Hu China	7	2.3k 1.1×	767 1.1×	577 1.1×	432 0.9×	497 1.1×	17	4.6k
Zhengyan Zhang China	16	2.7k 1.4×	847 1.3×	597 1.1×	532 1.1×	528 1.1×	35	4.9k
Takuya Akiba Japan	14	1.2k 0.6×	603 0.9×	246 0.5×	385 0.8×	304 0.7×	25	4.1k
M. Gori Italy	4	2.8k 1.4×	1.3k 1.9×	652 1.2×	576 1.2×	533 1.1×	6	5.3k
Markus Hagenbuchner Australia	17	3.1k 1.6×	1.5k 2.2×	820 1.5×	640 1.3×	566 1.2×	62	6.1k
Álex Rodríguez Italy	21	1.9k 0.9×	1.1k 1.6×	302 0.6×	593 1.2×	978 2.1×	70	4.8k
Cheng Yang China	28	3.5k 1.8×	1.0k 1.5×	1.2k 2.3×	1.2k 2.3×	677 1.5×	148	6.5k
Gabriele Monfardini Italy	7	3.4k 1.7×	1.6k 2.3×	770 1.4×	707 1.4×	612 1.3×	8	6.4k
Lifang He China	33	2.2k 1.1×	1.0k 1.5×	431 0.8×	563 1.1×	319 0.7×	175	4.2k
Zhao Li China	36	4.3k 2.2×	1.1k 1.6×	1.3k 2.4×	493 1.0×	409 0.9×	450	7.2k

Countries citing papers authored by Ganqu Cui

Since Specialization

Citations

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

Fields of papers citing papers by Ganqu Cui

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ganqu Cui

This figure shows the co-authorship network connecting the top 25 collaborators of Ganqu Cui. A scholar is included among the top collaborators of Ganqu 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 Ganqu Cui. Ganqu Cui 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.

Yu, Tianyu, Qiming Li, Yuan Yao, et al.. (2025). RLAIF-V: Open-Source AI Feedback Leads to Super GPT-4V Trustworthiness. 19985–19995.

2.

Wang, Shuo, et al.. (2024). INTERVENOR: Prompting the Coding Ability of Large Language Models with the Interactive Chain of Repair. 2081–2107. 3 indexed citations

3.

Ding, Ning, et al.. (2024). Scalable Efficient Training of Large Language Models with Low-dimensional Projected Attention. 14588–14599.

4.

Yu, Tianyu, Yuan Yao, Yifeng Han, et al.. (2024). RLHF-V: Towards Trustworthy MLLMs via Behavior Alignment from Fine-Grained Correctional Human Feedback. 13807–13816. 18 indexed citations

5.

Cui, Ganqu, Ning Ding, Zhenyu Sun, et al.. (2024). Controllable Preference Optimization: Toward Controllable Multi-Objective Alignment. 1437–1454. 1 indexed citations

6.

Cui, Ganqu, et al.. (2024). UltraMedical: Building Specialized Generalists in Biomedicine. 26045–26081. 2 indexed citations

7.

Cui, Ganqu, et al.. (2023). A Close Look into the Calibration of Pre-trained Language Models. 1343–1367. 5 indexed citations

8.

Ding, Ning, Yulin Chen, Ganqu Cui, et al.. (2023). Few-shot Classification with Hypersphere Modeling of Prototypes. 895–917. 4 indexed citations

9.

Cui, Ganqu, Yujia Qin, Chong Fu, et al.. (2023). Removing Backdoors in Pre-trained Models by Regularized Continual Pre-training. Transactions of the Association for Computational Linguistics. 11. 1608–1623.

10.

Chen, Yang‐Yi, Hongcheng Gao, Ganqu Cui, et al.. (2023). From Adversarial Arms Race to Model-centric Evaluation: Motivating a Unified Automatic Robustness Evaluation Framework. 4 indexed citations

11.

Cui, Ganqu, et al.. (2023). Beat LLMs at Their Own Game: Zero-Shot LLM-Generated Text Detection via Querying ChatGPT. 7470–7483. 4 indexed citations

12.

Cui, Ganqu, Wentao Li, Ning Ding, et al.. (2023). Decoder Tuning: Efficient Language Understanding as Decoding. 15072–15087. 2 indexed citations

13.

Chen, Yang‐Yi, Hongcheng Gao, Ganqu Cui, et al.. (2022). Why Should Adversarial Perturbations be Imperceptible? Rethink the Research Paradigm in Adversarial NLP. 11222–11237. 13 indexed citations

14.

Xu, Lei, et al.. (2022). Exploring the Universal Vulnerability of Prompt-based Learning Paradigm. 1799–1810. 21 indexed citations

15.

Yang, Cheng, Hao Wang, Jian Tang, et al.. (2021). Full-Scale Information Diffusion Prediction With Reinforced Recurrent Networks. IEEE Transactions on Neural Networks and Learning Systems. 34(5). 2271–2283. 27 indexed citations

16.

Zhou, Jie, Ganqu Cui, Shengding Hu, et al.. (2020). Graph neural networks: A review of methods and applications. SHILAP Revista de lepidopterología. 1. 57–81. 3221 indexed citations breakdown →

17.

Yu, Wenjian, et al.. (2020). Capacitance Extraction and Power Grid Analysis Using Statistical and AI Methods. 428–433. 1 indexed citations

18.

Cui, Ganqu, et al.. (2019). Machine-Learning-Driven Matrix Ordering for Power Grid Analysis. 984–987. 9 indexed citations

19.

Yang, Cheng, Jian Tang, Maosong Sun, Ganqu Cui, & Zhiyuan Liu. (2019). Multi-scale Information Diffusion Prediction with Reinforced Recurrent Networks. 4033–4039. 55 indexed citations

20.

Zhou, Jie, Ganqu Cui, Shengding Hu, et al.. (2018). Graph Neural Networks: A Review of Methods and Applications. arXiv (Cornell University). 767 indexed citations breakdown →

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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