Cheng Yang

11.4k total citations · 3 hit papers
148 papers, 6.5k citations indexed

About

Cheng Yang is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Cheng Yang has authored 148 papers receiving a total of 6.5k indexed citations (citations by other indexed papers that have themselves been cited), including 81 papers in Artificial Intelligence, 33 papers in Information Systems and 29 papers in Computer Vision and Pattern Recognition. Recurrent topics in Cheng Yang's work include Advanced Graph Neural Networks (40 papers), Topic Modeling (31 papers) and Complex Network Analysis Techniques (20 papers). Cheng Yang is often cited by papers focused on Advanced Graph Neural Networks (40 papers), Topic Modeling (31 papers) and Complex Network Analysis Techniques (20 papers). Cheng Yang collaborates with scholars based in China, United States and Singapore. Cheng Yang's co-authors include Maosong Sun, Zhiyuan Liu, Ganqu Cui, Zhengyan Zhang, Shengding Hu, Changcheng Li, Jie Zhou, Lifeng Wang, Chuan Shi and Edward Yi Chang and has published in prestigious journals such as Angewandte Chemie International Edition, SHILAP Revista de lepidopterología and Nano Letters.

In The Last Decade

Cheng Yang

125 papers receiving 6.3k citations

Hit Papers

Graph neural networks: A review of methods and ... 2015 2026 2018 2022 2020 2018 2015 1000 2.0k 3.0k
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. Graph neural networks: A review of methods and applications
    2020 3.2k citations Cheng Yang, Zhiyuan Liu et al. profile →
  2. Graph Neural Networks: A Review of Methods and Applications
    2018 767 citations Cheng Yang, Zhiyuan Liu et al. arXiv (Cornell University) profile →
  3. Network representation learning with rich text information
    2015 524 citations Cheng Yang, Zhiyuan Liu et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Cheng Yang China 28 3.5k 1.2k 1.2k 1.0k 681 148 6.5k
Markus Hagenbuchner Australia 17 3.1k 0.9× 820 0.7× 640 0.6× 1.5k 1.4× 637 0.9× 62 6.1k
Gabriele Monfardini Italy 7 3.4k 1.0× 770 0.6× 707 0.6× 1.6k 1.5× 675 1.0× 8 6.4k
Franco Scarselli Italy 23 4.3k 1.2× 1.1k 0.9× 1000 0.9× 2.1k 2.0× 887 1.3× 77 8.4k
Jianxin Li China 44 2.6k 0.7× 1.3k 1.1× 940 0.8× 822 0.8× 1.4k 2.0× 398 7.1k
Aditya Grover United States 11 4.5k 1.3× 1.5k 1.2× 2.3k 2.0× 826 0.8× 748 1.1× 29 7.5k
Zhengyan Zhang China 16 2.7k 0.8× 597 0.5× 532 0.5× 847 0.8× 486 0.7× 35 4.9k
M. Gori Italy 4 2.8k 0.8× 652 0.5× 576 0.5× 1.3k 1.2× 582 0.9× 6 5.3k
Jia Wu Australia 46 4.5k 1.3× 1.5k 1.2× 1.1k 1.0× 1.8k 1.7× 780 1.1× 356 7.8k
Shengding Hu China 7 2.3k 0.6× 577 0.5× 432 0.4× 767 0.7× 460 0.7× 17 4.6k
Zhao Li China 36 4.3k 1.2× 1.3k 1.1× 493 0.4× 1.1k 1.0× 1.1k 1.5× 450 7.2k

Countries citing papers authored by Cheng Yang

Since Specialization
Citations

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

Fields of papers citing papers by Cheng Yang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Cheng Yang

This figure shows the co-authorship network connecting the top 25 collaborators of Cheng Yang. A scholar is included among the top collaborators of Cheng Yang 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 Cheng Yang. Cheng Yang 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.
Yang, Cheng, et al.. (2025). Data-centric Prompt Tuning for Dynamic Graphs. 2336–2345.
2.
Wang, Xiao, et al.. (2025). Can Large Language Models Improve the Adversarial Robustness of Graph Neural Networks?. 2008–2019. 2 indexed citations
3.
Liu, Jiawei, Cheng Yang, Zhiyuan Lu, et al.. (2025). Graph Foundation Models: Concepts, Opportunities and Challenges. IEEE Transactions on Pattern Analysis and Machine Intelligence. 47(6). 5023–5044. 8 indexed citations
4.
Yang, Chengdong, et al.. (2025). FLAG: Fraud Detection with LLM-enhanced Graph Neural Network. 5150–5160.
5.
Bai, Ting, et al.. (2025). Efficient Multi-task Prompt Tuning for Recommendation. ACM Transactions on Information Systems. 43(4). 1–21.
6.
7.
Yang, Cheng, Chao Jiang, Guo Yu, Jun Li, & Cuimei Bo. (2024). APSF-Net: A deep adversarial slow feature extraction network for industrial inferential modeling. Control Engineering Practice. 147. 105934–105934. 1 indexed citations
8.
Yang, Cheng, et al.. (2024). Study on the Correctional Treatment of Juvenile Delinquency and Social Work Intervention Mechanisms in China. International Journal of Education and Humanities. 13(2). 138–140.
9.
Yang, Cheng, Chao Jiang, Guo Yu, Jun Li, & Cuimei Bo. (2024). Transferable adversarial slow feature extraction network for few-shot quality prediction in coal-to-ethylene glycol process. Chinese Journal of Chemical Engineering. 71. 258–271. 1 indexed citations
10.
Yang, Cheng, et al.. (2024). Endowing Pre-trained Graph Models with Provable Fairness. arXiv (Cornell University). 1045–1056. 4 indexed citations
11.
Bo, Deyu, et al.. (2024). Data-Centric Graph Learning: A Survey. IEEE Transactions on Big Data. 11(1). 1–20. 3 indexed citations
12.
Liu, Yulin, Xuan Wang, Lan Ma, et al.. (2023). Discovery of novel and bioavailable histone deacetylases and cyclin-dependent kinases dual inhibitor to impair the stemness of leukemia cells. European Journal of Medicinal Chemistry. 249. 115140–115140. 6 indexed citations
13.
Shi, Chuan, et al.. (2023). Distance Information Improves Heterogeneous Graph Neural Networks. IEEE Transactions on Knowledge and Data Engineering. 36(3). 1030–1043. 2 indexed citations
14.
Yang, Cheng, et al.. (2023). Learning to Distill Graph Neural Networks. 123–131. 7 indexed citations
15.
Yang, Cheng, et al.. (2023). GammaGL: A Multi-Backend Library for Graph Neural Networks. 2861–2870. 2 indexed citations
16.
Yang, Cheng, et al.. (2023). Knowledge-Adaptive Contrastive Learning for Recommendation. 535–543. 43 indexed citations
17.
18.
Hu, Linmei, Chen Li, Cheng Yang, et al.. (2020). Graph Neural News Recommendation with Unsupervised Preference Disentanglement. 4255–4264. 82 indexed citations
19.
Hu, Linmei, et al.. (2019). Improving Distantly-Supervised Relation Extraction with Joint Label Embedding. Monash University Research Portal (Monash University). 3819–3827. 30 indexed citations
20.
Wang, Songtao, Dan Li, Cheng Yang, et al.. (2018). BML: A High-performance, Low-cost Gradient Synchronization Algorithm for DML Training. Neural Information Processing Systems. 31. 4238–4248. 17 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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