Noise Perturbation Based Graph Contrastive Learning via Flexible Filters for Node Classification-Reference-Cited by-同舟云学术

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Noise Perturbation Based Graph Contrastive Learning via Flexible Filters for Node Classification

Published:2024-06-30 Issue: Volume: Page:1-10
ISSN:
Container-title:2024 International Joint Conference on Neural Networks (IJCNN)
language:
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Author:

Xiong Zhilong¹,Cai Jia²,Yan Ranhui³,Huang Xiaolin⁴

Affiliation:

1. xFusion Digital Technologies Company Limited,Shenzhen,China

2. Guangdong University of Finance & Economics,School of Digital Economics,Guangzhou,China

3. Guangdong University of Finance & Economics,School of Statistics and Mathematics,Guangzhou,China

4. Shanghai Jiao Tong University,Institute of Image Processing and Pattern Recognition,Shanghai,China

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10649807/10649898/10650206.pdf?arnumber=10650206

Reference32 articles.

1. DeepWalk

2. Mining heterogeneous information networks

3. Learning to Hash with Graph Neural Networks for Recommender Systems

4. Graph Convolutional Networks for Text Classification

5. Very deep graph neural networks via noise regularisation;Godwin,2021

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