Self-Supervised Guided Hypergraph Feature Propagation for Semi-Supervised Classification with Missing Node Features-Reference-Cited by-同舟云学术

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Self-Supervised Guided Hypergraph Feature Propagation for Semi-Supervised Classification with Missing Node Features

Published:2023-06-04 Issue: Volume: Page:
ISSN:
Container-title:ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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Author:

Lei Chengxiang¹,Fu Sichao¹,Wang Yuetian¹,Qiu Wenhao¹,Hu Yachen¹,Peng Qinmu¹,You Xinge¹

Affiliation:

1. Huazhong University of Science and Technology,School of Electronic Information and Communications

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10094559/10094560/10094867.pdf?arnumber=10094867

Reference21 articles.

1. Graph convolutional networks for graphs containing missing features

2. Learning on attribute-missing graphs;chen;IEEE Transactions on Pattern Analysis and Machine Intelligence,2020

3. Semi-supervised classification with graph convolutional networks;welling;Proceedings of the International Conference on Learning Representations,2017

4. Learning from labeled and unlabeled data with label propagation;xiaojin;Technical Report CMU-CALD-05–103,2002

Cited by 1 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. Towards Unsupervised Graph Completion Learning on Graphs with Features and Structure Missing;2023 IEEE International Conference on Data Mining (ICDM);2023-12-01

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