Pseudo-Labeling with Graph Active Learning for Few-shot Node Classification-Reference-Cited by-同舟云学术

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Pseudo-Labeling with Graph Active Learning for Few-shot Node Classification

Published:2023-12-01 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE International Conference on Data Mining (ICDM)
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Author:

Li Quan¹,Chen Lingwei²,Jing Shixiong¹,Wu Dinghao¹

Affiliation:

1. Pennsylvania State University, University Park,College of Information Sciences and Technology,PA,USA

2. Wright State University,Department of Computer Science and Engineering,Dayton,OH,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10415628/10415561/10415762.pdf?arnumber=10415762

Reference36 articles.

1. Graph Neural Networks in Recommender Systems: A Survey

2. Hierarchical Graph Neural Network for Patient Treatment Preference Prediction with External Knowledge

3. HOVER: Homophilic Oversampling via Edge Removal for Class-Imbalanced Bot Detection on Graphs

4. DeepRank-GNN: a graph neural network framework to learn patterns in protein–protein interfaces

5. Semi-supervised classification with graph convolutional networks;Kipf;arXiv preprint arXiv:1609.02907,2016

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. DOS-GNN: Dual-Feature Aggregations with Over-Sampling for Class-Imbalanced Fraud Detection On Graphs;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

2. H$$^2$$GNN: Graph Neural Networks with Homophilic and Heterophilic Feature Aggregations;Lecture Notes in Computer Science;2024

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