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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Graph Prototypical Networks for Few-shot Learning on Attributed Networks
Kaize Ding, Jianling Wang, Jundong Li, et al.
(2020)
Open Access | Times Cited: 106

Showing 1-25 of 106 citing articles:

Be More with Less: Hypergraph Attention Networks for Inductive Text Classification
Kaize Ding, Jianling Wang, Jundong Li, et al.
(2020)
Open Access | Times Cited: 155
Data Augmentation for Deep Graph Learning
Kaize Ding, Zhe Xu, Hanghang Tong, et al.
ACM SIGKDD Explorations Newsletter (2022) Vol. 24, Iss. 2, pp. 61-77
Closed Access | Times Cited: 125
Few-shot Network Anomaly Detection via Cross-network Meta-learning
Kaize Ding, Qinghai Zhou, Hanghang Tong, et al.
(2021)
Open Access | Times Cited: 81
Learning to Affiliate: Mutual Centralized Learning for Few-shot Classification
Yang Liu, Weifeng Zhang, Chao Xiang, et al.
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022), pp. 14391-14400
Open Access | Times Cited: 66
PMR: Prototypical Modal Rebalance for Multimodal Learning
Yunfeng Fan, Wenchao Xu, Haozhao Wang, et al.
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2023), pp. 20029-20038
Open Access | Times Cited: 31
Graph Few-shot Learning with Attribute Matching
Ning Wang, Minnan Luo, Kaize Ding, et al.
(2020)
Closed Access | Times Cited: 50
Spatio-Temporal Graph Few-Shot Learning with Cross-City Knowledge Transfer
Bin Lü, Xiaoying Gan, Weinan Zhang, et al.
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2022), pp. 1162-1172
Open Access | Times Cited: 36
Imbalanced Graph Classification via Graph-of-Graph Neural Networks
Yu Wang, Yuying Zhao, Neil Shah, et al.
Proceedings of the 31st ACM International Conference on Information & Knowledge Management (2022)
Open Access | Times Cited: 32
Meta Propagation Networks for Graph Few-shot Semi-supervised Learning
Kaize Ding, Jianling Wang, James Caverlee, et al.
Proceedings of the AAAI Conference on Artificial Intelligence (2022) Vol. 36, Iss. 6, pp. 6524-6531
Open Access | Times Cited: 32
Graph Few-shot Class-incremental Learning
Zhen Tan, Kaize Ding, Ruocheng Guo, et al.
Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining (2022), pp. 987-996
Open Access | Times Cited: 29
Sequential Recommendation for Cold-start Users with Meta Transitional Learning
Jianling Wang, Kaize Ding, James Caverlee
Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (2021)
Open Access | Times Cited: 34
Task-Adaptive Few-shot Node Classification
Song Wang, Kaize Ding, Chuxu Zhang, et al.
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2022), pp. 1910-1919
Open Access | Times Cited: 25
Eliciting Structural and Semantic Global Knowledge in Unsupervised Graph Contrastive Learning
Kaize Ding, Yan-Cheng Wang, Shuicheng Yan, et al.
Proceedings of the AAAI Conference on Artificial Intelligence (2023) Vol. 37, Iss. 6, pp. 7378-7386
Open Access | Times Cited: 15
Federated Few-shot Learning
Song Wang, Xingbo Fu, Kaize Ding, et al.
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2023)
Open Access | Times Cited: 15
Toward Robust Graph Semi-Supervised Learning Against Extreme Data Scarcity
Kaize Ding, Elnaz Nouri, Guo‐qing Zheng, et al.
IEEE Transactions on Neural Networks and Learning Systems (2024) Vol. 35, Iss. 9, pp. 11661-11670
Open Access | Times Cited: 5
Cross-Domain Graph Anomaly Detection
Kaize Ding, Kai Shu, Xuan Shan, et al.
IEEE Transactions on Neural Networks and Learning Systems (2021) Vol. 33, Iss. 6, pp. 2406-2415
Closed Access | Times Cited: 27
Informative pseudo-labeling for graph neural networks with few labels
Yayong Li, Jie Yin, Ling Chen
Data Mining and Knowledge Discovery (2022) Vol. 37, Iss. 1, pp. 228-254
Open Access | Times Cited: 21
Few-shot Node Classification with Extremely Weak Supervision
Song Wang, Yushun Dong, Kaize Ding, et al.
(2023)
Open Access | Times Cited: 12
Few-Shot Graph Anomaly Detection via Dual-Level Knowledge Distillation
Xuan Li, Defu Cheng, L H Zhang, et al.
Entropy (2025) Vol. 27, Iss. 1, pp. 28-28
Open Access
Hierarchical global to local calibration for query-focused few-shot node classification
Shuzhen Rao, Jun Huang, Zengming Tang
Information Fusion (2025), pp. 102929-102929
Closed Access
Enhancing Unsupervised Graph Few-shot Learning via Set Functions and Optimal Transport
Yonghao Liu, Fausto Giunchiglia, Ximing Li, et al.
(2025), pp. 871-882
Open Access
Prototypical networks with unlabeled data for few-shot node classification
Nengchao Wang, Yujing Lai, Chuan Chen, et al.
Engineering Applications of Artificial Intelligence (2025) Vol. 145, pp. 110088-110088
Closed Access
Information bottleneck-driven prompt on graphs for unifying downstream few-shot classification tasks
Xin Zhang, Wanyu Chen, Fei Cai, et al.
Information Processing & Management (2025) Vol. 62, Iss. 4, pp. 104092-104092
Closed Access
Meta-learning Assisted Graph Representation Learning for Downstream Learning Tasks
N. R. Bhat, Prasad P. Kulkarni, Aswathy Menon, et al.
Lecture notes in electrical engineering (2025), pp. 31-44
Closed Access
UniGraph: Learning a Unified Cross-Domain Foundation Model for Text-Attributed Graphs
Yufei He, Yuan Sui, Xiaoxin He, et al.
(2025), pp. 448-459
Closed Access
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