Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labels-Reference-Cited by-同舟云学术

Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labels

Published:2022-02-11 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining
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Author:

Dai Enyan¹,Jin Wei²,Liu Hui²,Wang Suhang¹

Affiliation:

1. The Pennsylvania State University, State College, PA, USA

2. Michigan State University, East Lansing, MI, USA

Funder

National Science Foundation

Army Research Office

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3488560.3498408

Reference46 articles.

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3. Ming Chen Zhewei Wei Zengfeng Huang Bolin Ding and Yaliang Li. 2020. Simple and deep graph convolutional networks. In ICML. PMLR 1725--1735. Ming Chen Zhewei Wei Zengfeng Huang Bolin Ding and Yaliang Li. 2020. Simple and deep graph convolutional networks. In ICML. PMLR 1725--1735.

4. Zhao-Min Chen Xiu-Shen Wei Peng Wang and Yanwen Guo. 2019. Multi-label image recognition with graph convolutional networks. In CVPR . 5177--5186. Zhao-Min Chen Xiu-Shen Wei Peng Wang and Yanwen Guo. 2019. Multi-label image recognition with graph convolutional networks. In CVPR . 5177--5186.

5. Wei-Lin Chiang Xuanqing Liu Si Si Yang Li Samy Bengio and Cho-Jui Hsieh. 2019. Cluster-GCN: An efficient algorithm for training deep and large graph convolutional networks. In SIGKDD. 257--266. Wei-Lin Chiang Xuanqing Liu Si Si Yang Li Samy Bengio and Cho-Jui Hsieh. 2019. Cluster-GCN: An efficient algorithm for training deep and large graph convolutional networks. In SIGKDD. 257--266.

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