Guolin Ke

13.2k total citations · 3 hit papers
22 papers, 8.0k citations indexed

About

Guolin Ke is a scholar working on Artificial Intelligence, Materials Chemistry and Computer Vision and Pattern Recognition. According to data from OpenAlex, Guolin Ke has authored 22 papers receiving a total of 8.0k indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Artificial Intelligence, 7 papers in Materials Chemistry and 6 papers in Computer Vision and Pattern Recognition. Recurrent topics in Guolin Ke's work include Machine Learning in Materials Science (6 papers), Machine Learning and Data Classification (5 papers) and Topic Modeling (4 papers). Guolin Ke is often cited by papers focused on Machine Learning in Materials Science (6 papers), Machine Learning and Data Classification (5 papers) and Topic Modeling (4 papers). Guolin Ke collaborates with scholars based in China, United States and United Kingdom. Guolin Ke's co-authors include Tie‐Yan Liu, Qi Meng, Qiwei Ye, Weidong Ma, Taifeng Wang, Wei Chen, Thomas Finley, Yongchun Zhu, Jiang Bian and Jindong Wang and has published in prestigious journals such as Nature Communications, SHILAP Revista de lepidopterología and Nano Letters.

In The Last Decade

Guolin Ke

20 papers receiving 7.8k citations

Hit Papers

LightGBM: A Highly Efficient Gradient Boosting Decision Tree 2017 2026 2020 2023 2017 2020 2021 2.0k 4.0k 6.0k
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. LightGBM: A Highly Efficient Gradient Boosting Decision Tree
    2017 6.5k citations Guolin Ke, Qi Meng et al. profile →
  2. Deep Subdomain Adaptation Network for Image Classification
    2020 843 citations Yongchun Zhu, Fuzhen Zhuang et al. IEEE Transactions on Neural Networks and Learning Systems profile →
  3. Do Transformers Really Perform Badly for Graph Representation
    2021 248 citations Chengxuan Ying, Tianle Cai et al. Neural Information Processing Systems profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Guolin Ke China 13 2.5k 805 708 624 613 22 8.0k
Zigang Lu China 6 2.1k 0.8× 687 0.9× 797 1.1× 660 1.1× 522 0.9× 17 8.8k
Taifeng Wang China 15 2.3k 0.9× 716 0.9× 574 0.8× 600 1.0× 647 1.1× 33 7.4k
Davide Chicco Canada 22 2.5k 1.0× 652 0.8× 642 0.9× 560 0.9× 1.3k 2.1× 70 9.5k
Qi Meng China 9 1.9k 0.8× 731 0.9× 444 0.6× 611 1.0× 533 0.9× 21 6.7k
Weidong Ma China 13 1.9k 0.8× 721 0.9× 382 0.5× 633 1.0× 529 0.9× 50 6.9k
Qiwei Ye China 5 1.8k 0.7× 718 0.9× 389 0.5× 603 1.0× 529 0.9× 8 6.7k
Darrell Whitley United States 31 3.2k 1.2× 886 1.1× 559 0.8× 589 0.9× 356 0.6× 143 7.6k
Thomas Finley United States 11 2.8k 1.1× 717 0.9× 1.1k 1.5× 609 1.0× 598 1.0× 17 8.0k
Giuseppe Jurman Italy 28 2.3k 0.9× 626 0.8× 619 0.9× 577 0.9× 1.7k 2.8× 97 9.9k
Stephan R. Sain United States 27 2.0k 0.8× 451 0.6× 1.1k 1.5× 836 1.3× 424 0.7× 85 7.2k

Countries citing papers authored by Guolin Ke

Since Specialization
Citations

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

Fields of papers citing papers by Guolin Ke

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Guolin Ke

This figure shows the co-authorship network connecting the top 25 collaborators of Guolin Ke. A scholar is included among the top collaborators of Guolin Ke 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 Guolin Ke. Guolin Ke 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.
Gao, Zhifeng, Siyuan Liu, Hongshuai Wang, et al.. (2025). End‐to‐End Crystal Structure Prediction from Powder X‐Ray Diffraction. Advanced Science. 12(8). e2410722–e2410722. 9 indexed citations
2.
Meng, Yuan, Junxiang Li, Ning Li, et al.. (2025). Deep learning for sub-ångström-resolution imaging in uncorrected scanning transmission electron microscopy. National Science Review. 12(8). nwaf235–nwaf235.
3.
Yao, Lin, et al.. (2024). Node-Aligned Graph-to-Graph: Elevating Template-free Deep Learning Approaches in Single-Step Retrosynthesis. SHILAP Revista de lepidopterología. 4(3). 992–1003. 17 indexed citations
4.
Zhou, Gengmo, Zhengdan Zhu, Guolin Ke, et al.. (2024). Bridging Machine Learning and Thermodynamics for Accurate p K a Prediction. SHILAP Revista de lepidopterología. 4(9). 3451–3465. 15 indexed citations
5.
Gao, Zhifeng, et al.. (2024). Data-driven quantum chemical property prediction leveraging 3D conformations with Uni-Mol+. Nature Communications. 15(1). 7104–7104. 22 indexed citations
6.
Wang, Jingqi, Jiapeng Liu, Hongshuai Wang, et al.. (2024). A comprehensive transformer-based approach for high-accuracy gas adsorption predictions in metal-organic frameworks. Nature Communications. 15(1). 1904–1904. 62 indexed citations
7.
Li, Ziyao, et al.. (2024). Accurate Conformation Sampling via Protein Structural Diffusion. Journal of Chemical Information and Modeling. 64(22). 8414–8426. 5 indexed citations
8.
Han, Xiaocang, Yuan Meng, Xiang Chen, et al.. (2024). Single-Image-Based Deep Learning for Precise Atomic Defect Identification. Nano Letters. 24(33). 10275–10283. 1 indexed citations
9.
10.
Cheng, Zheng, Jiapeng Liu, Tong Jiang, et al.. (2023). Automatic Screen‐out of Ir(III) Complex Emitters by Combined Machine Learning and Computational Analysis. Advanced Optical Materials. 11(18). 8 indexed citations
11.
Ke, Guolin, Di He, & Tie‐Yan Liu. (2021). Rethinking Positional Encoding in Language Pre-training. International Conference on Learning Representations. 82 indexed citations
12.
Ying, Chengxuan, Tianle Cai, Shengjie Luo, et al.. (2021). Do Transformers Really Perform Badly for Graph Representation. Neural Information Processing Systems. 34. 248 indexed citations breakdown →
13.
He, Di, Chenyan Xiong, Guolin Ke, et al.. (2021). Less is More: Pretrain a Strong Siamese Encoder for Dense Text Retrieval Using a Weak Decoder. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 2780–2791. 33 indexed citations
14.
Ke, Guolin, James Lamb, Thomas Finley, et al.. (2021). Light Gradient Boosting Machine [R package lightgbm version 3.2.0]. 1 indexed citations
15.
Chen, Xing, et al.. (2021). Taking Notes on the Fly Helps Language Pre-Training. 9 indexed citations
16.
Zhu, Yongchun, Fuzhen Zhuang, Jindong Wang, et al.. (2020). Deep Subdomain Adaptation Network for Image Classification. IEEE Transactions on Neural Networks and Learning Systems. 32(4). 1713–1722. 843 indexed citations breakdown →
17.
Ke, Guolin, et al.. (2020). Light Multi-Segment Activation for Model Compression. Proceedings of the AAAI Conference on Artificial Intelligence. 34(4). 6542–6549. 2 indexed citations
18.
Ke, Guolin, et al.. (2019). DeepGBM. 384–394. 86 indexed citations
19.
Ke, Guolin, et al.. (2017). A Highly Efficient Gradient Boosting Decision Tree. Neural Information Processing Systems. 3108–3116. 46 indexed citations
20.
Meng, Qi, Guolin Ke, Wei Chen, et al.. (2016). A Communication-Efficient Parallel Algorithm for Decision Tree. arXiv (Cornell University). 29. 1271–1279. 52 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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