Kaize Ding

2.8k total citations · 1 hit paper
56 papers, 1.1k citations indexed

About

Kaize Ding is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Kaize Ding has authored 56 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 43 papers in Artificial Intelligence, 12 papers in Information Systems and 9 papers in Computer Vision and Pattern Recognition. Recurrent topics in Kaize Ding's work include Advanced Graph Neural Networks (31 papers), Topic Modeling (15 papers) and Domain Adaptation and Few-Shot Learning (13 papers). Kaize Ding is often cited by papers focused on Advanced Graph Neural Networks (31 papers), Topic Modeling (15 papers) and Domain Adaptation and Few-Shot Learning (13 papers). Kaize Ding collaborates with scholars based in United States, China and Australia. Kaize Ding's co-authors include Huan Liu, Jundong Li, Jianling Wang, Hanghang Tong, James Caverlee, Liangjie Hong, Zhe Xu, Kai Shu, Dingcheng Li and Mansooreh Karami and has published in prestigious journals such as Scientific Reports, IEEE Transactions on Neural Networks and Learning Systems and Information Fusion.

In The Last Decade

Kaize Ding

48 papers receiving 1.1k citations

Hit Papers

Data Augmentation for Deep Graph Learning 2022 2026 2023 2024 2022 40 80 120
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. Data Augmentation for Deep Graph Learning
    2022 128 citations Kaize Ding, Huan 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
Kaize Ding United States 17 924 335 201 173 134 56 1.1k
Mohamed Aly United States 13 659 0.7× 345 1.0× 185 0.9× 90 0.5× 83 0.6× 28 1.1k
Kan Li China 19 429 0.5× 270 0.8× 115 0.6× 242 1.4× 119 0.9× 99 984
Linmei Hu China 16 961 1.0× 520 1.6× 72 0.4× 109 0.6× 269 2.0× 46 1.3k
Shiwan Zhao China 15 545 0.6× 482 1.4× 108 0.5× 91 0.5× 65 0.5× 56 917
Rui Xia China 24 1.4k 1.6× 273 0.8× 90 0.4× 62 0.4× 150 1.1× 47 1.7k
Justin Zhan United States 19 669 0.7× 317 0.9× 167 0.8× 125 0.7× 118 0.9× 69 1.1k
Jinoh Oh South Korea 14 662 0.7× 812 2.4× 153 0.8× 72 0.4× 137 1.0× 30 1.1k
Yuan Yao China 18 425 0.5× 525 1.6× 146 0.7× 75 0.4× 87 0.6× 77 947
Zhuang Li China 11 907 1.0× 399 1.2× 223 1.1× 43 0.2× 118 0.9× 47 1.3k

Countries citing papers authored by Kaize Ding

Since Specialization
Citations

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

Fields of papers citing papers by Kaize Ding

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kaize Ding

This figure shows the co-authorship network connecting the top 25 collaborators of Kaize Ding. A scholar is included among the top collaborators of Kaize Ding 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 Kaize Ding. Kaize Ding 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.
2.
He, Jianfeng, Changbin Li, Min Zhang, et al.. (2025). Survey of uncertainty estimation in LLMs - Sources, methods, applications, and challenges. Information Fusion. 130. 104057–104057.
3.
Ding, Kaize, et al.. (2025). Optimization and characterization of cellulose extraction from cotton straw using alkali pretreatment. Biomass Conversion and Biorefinery. 15(16). 22637–22650. 1 indexed citations
4.
5.
Ma, Xiaoxiao, et al.. (2024). On Fake News Detection with LLM Enhanced Semantics Mining. 508–521. 8 indexed citations
6.
Zhang, Chuxu, Dongkuan Xu, Kaize Ding, et al.. (2024). RelKD 2024: The Second International Workshop on Resource-Efficient Learning for Knowledge Discovery. 6749–6750.
7.
Ding, Kaize, Xiaoxiao Ma, Yixin Liu, & Shirui Pan. (2024). Divide and Denoise: Empowering Simple Models for Robust Semi-Supervised Node Classification against Label Noise. 574–584. 5 indexed citations
8.
Jing, Baoyu, Kaize Ding, Yada Zhu, et al.. (2024). Mastering Long-Tail Complexity on Graphs: Characterization, Learning, and Generalization. VTechWorks (Virginia Tech). 3045–3056. 4 indexed citations
9.
Wang, Yu, Kaize Ding, Xiaorui Liu, et al.. (2024). Data Quality-aware Graph Machine Learning. 5534–5537. 1 indexed citations
10.
Ding, Kaize, et al.. (2024). Empowering Large Language Models for Textual Data Augmentation. 12734–12751. 3 indexed citations
11.
Ding, Kaize, Yixin Liu, Chuxu Zhang, & Jian-Ling Wang. (2024). Data‐efficient graph learning: Problems, progress, and prospects. AI Magazine. 45(4). 549–560. 1 indexed citations
12.
Chen, Zhengyu, et al.. (2024). Let’s Ask GNN: Empowering Large Language Model for Graph In-Context Learning. 1396–1409. 1 indexed citations
13.
Ding, Kaize, Yan-Cheng Wang, Shuicheng Yan, & Huan Liu. (2023). Eliciting Structural and Semantic Global Knowledge in Unsupervised Graph Contrastive Learning. Proceedings of the AAAI Conference on Artificial Intelligence. 37(6). 7378–7386. 16 indexed citations
14.
Wang, Song, et al.. (2023). Federated Few-shot Learning. 2374–2385. 17 indexed citations
15.
Liu, Yixin, Kaize Ding, Jianling Wang, et al.. (2023). Learning Strong Graph Neural Networks with Weak Information. arXiv (Cornell University). 1559–1571. 24 indexed citations
16.
Ding, Kaize, Bryan Perozzi, Lichan Hong, et al.. (2023). HyperFormer: Learning Expressive Sparse Feature Representations via Hypergraph Transformer. 2062–2066. 4 indexed citations
17.
Ding, Kaize, Jianling Wang, Jundong Li, James Caverlee, & Huan Liu. (2023). Robust Graph Meta-Learning for Weakly Supervised Few-Shot Node Classification. ACM Transactions on Knowledge Discovery from Data. 18(4). 1–18. 4 indexed citations
18.
19.
Ding, Kaize, Jianling Wang, Jundong Li, Dingcheng Li, & Huan Liu. (2020). Be More with Less: Hypergraph Attention Networks for Inductive Text Classification. 4927–4936. 128 indexed citations
20.
Ding, Kaize, et al.. (2019). Graph Neural Networks with High-order Feature Interactions.. arXiv (Cornell University). 3 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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