Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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:

GHNN: Graph Harmonic Neural Networks for semi-supervised graph-level classification
Wei Ju, Xiao Luo, Zeyu Ma, et al.
Neural Networks (2022) Vol. 151, pp. 70-79
Closed Access | Times Cited: 39

Showing 1-25 of 39 citing articles:

A Comprehensive Survey on Deep Graph Representation Learning
Wei Ju, Zheng Fang, Yiyang Gu, et al.
Neural Networks (2024) Vol. 173, pp. 106207-106207
Open Access | Times Cited: 133
Machine learning-based fatigue life prediction of metal materials: Perspectives of physics-informed and data-driven hybrid methods
Haijie Wang, Bo Li, Jian‐Guo Gong, et al.
Engineering Fracture Mechanics (2023) Vol. 284, pp. 109242-109242
Closed Access | Times Cited: 120
Unsupervised graph-level representation learning with hierarchical contrasts
Wei Ju, Yiyang Gu, Xiao Luo, et al.
Neural Networks (2022) Vol. 158, pp. 359-368
Closed Access | Times Cited: 47
Few-shot Molecular Property Prediction via Hierarchically Structured Learning on Relation Graphs
Wei Ju, Zequn Liu, Yifang Qin, et al.
Neural Networks (2023) Vol. 163, pp. 122-131
Closed Access | Times Cited: 35
Dynamic Hypergraph Structure Learning for Traffic Flow Forecasting
Yusheng Zhao, Xiao Luo, Wei Ju, et al.
2022 IEEE 38th International Conference on Data Engineering (ICDE) (2023), pp. 2303-2316
Open Access | Times Cited: 27
TGNN: A Joint Semi-supervised Framework for Graph-level Classification
Wei Ju, Xiao Luo, Meng Qu, et al.
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (2022)
Open Access | Times Cited: 29
RHGNN: Fake reviewer detection based on reinforced heterogeneous graph neural networks
Jun Zhao, Minglai Shao, Hailiang Tang, et al.
Knowledge-Based Systems (2023) Vol. 280, pp. 111029-111029
Closed Access | Times Cited: 14
Subgraph-Aware Graph Kernel Neural Network for Link Prediction in Biological Networks
Menglu Li, Zhiwei Wang, L. Liu, et al.
IEEE Journal of Biomedical and Health Informatics (2024) Vol. 28, Iss. 7, pp. 4373-4381
Closed Access | Times Cited: 5
Focus on informative graphs! Semi-supervised active learning for graph-level classification
Wei Ju, Zhengyang Mao, Ziyue Qiao, et al.
Pattern Recognition (2024) Vol. 153, pp. 110567-110567
Closed Access | Times Cited: 5
Multi-graph Fusion Graph Convolutional Networks with pseudo-label supervision
Yachao Yang, Yanfeng Sun, Fujiao Ju, et al.
Neural Networks (2022) Vol. 158, pp. 305-317
Closed Access | Times Cited: 21
On exploring node-feature and graph-structure diversities for node drop graph pooling
Chuang Liu, Yibing Zhan, Baosheng Yu, et al.
Neural Networks (2023) Vol. 167, pp. 559-571
Open Access | Times Cited: 12
SMGCL: Semi-supervised Multi-view Graph Contrastive Learning
Hui Zhou, Maoguo Gong, Shanfeng Wang, et al.
Knowledge-Based Systems (2022) Vol. 260, pp. 110120-110120
Closed Access | Times Cited: 19
Learning on Graphs under Label Noise
Jingyang Yuan, Xiao Luo, Yifang Qin, et al.
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2023)
Open Access | Times Cited: 11
Complex Graph Analysis and Representation Learning: Problems, Techniques, and Applications
Xinjun Pei, Xiaoheng Deng, Naixue Xiong, et al.
IEEE Transactions on Network Science and Engineering (2024) Vol. 11, Iss. 5, pp. 4990-5007
Closed Access | Times Cited: 3
Adversarial Cluster-Level and Global-Level Graph Contrastive Learning for node representation
Qian Tang, Yiji Zhao, Hao Wu, et al.
Knowledge-Based Systems (2023) Vol. 279, pp. 110935-110935
Closed Access | Times Cited: 6
Towards Long-Tailed Recognition for Graph Classification via Collaborative Experts
Siyu Yi, Zhengyang Mao, Wei Ju, et al.
IEEE Transactions on Big Data (2023) Vol. 9, Iss. 6, pp. 1683-1696
Open Access | Times Cited: 6
Self-supervised robust Graph Neural Networks against noisy graphs and noisy labels
Jinliang Yuan, Hualei Yu, Meng Cao, et al.
Applied Intelligence (2023) Vol. 53, Iss. 21, pp. 25154-25170
Closed Access | Times Cited: 5
Improving Node Classification Accuracy of GNN through Input and Output Intervention
Anjan Chowdhury, Sriram Srinivasan, Animesh Mukherjee, et al.
ACM Transactions on Knowledge Discovery from Data (2023) Vol. 18, Iss. 1, pp. 1-31
Closed Access | Times Cited: 4
An effective targeted label adversarial attack on graph neural networks by strategically allocating the attack budget
Feilong Cao, Q. Chen, Hailiang Ye
Knowledge-Based Systems (2024) Vol. 293, pp. 111689-111689
Closed Access | Times Cited: 1
Node-Smoothness-Based Adaptive Initial Residual Deep Graph Convolutional Network
Hui Chen, Liguang Zang, Yuancheng Li
IEEE Internet of Things Journal (2024) Vol. 11, Iss. 13, pp. 23878-23888
Closed Access | Times Cited: 1
New approach for learning structured graph with Laplacian rank constraint
Yu Duan, Feiping Nie, Rong Wang, et al.
Neurocomputing (2024) Vol. 598, pp. 128065-128065
Closed Access | Times Cited: 1
GraphixMatch: Improving semi-supervised learning for graph classification with FixMatch
Eunji Koh, Young Jae Lee, Seoung Bum Kim
Neurocomputing (2024) Vol. 607, pp. 128356-128356
Closed Access | Times Cited: 1
Graph Batch Coarsening framework for scalable graph neural networks
Shengzhong Zhang, Yimin D. Zhang, Bisheng Li, et al.
Neural Networks (2024) Vol. 183, pp. 106931-106931
Closed Access | Times Cited: 1
A Comprehensive Survey on Deep Graph Representation Learning
Wei Ju, Zheng Fang, Yiyang Gu, et al.
arXiv (Cornell University) (2023)
Open Access | Times Cited: 3
DyVGRNN: DYnamic mixture Variational Graph Recurrent Neural Networks
Ghazaleh Niknam, Soheila Molaei, Hadi Zare, et al.
Neural Networks (2023) Vol. 165, pp. 596-610
Open Access | Times Cited: 3
Page 1 - Next Page

Scroll to top