Learning Strong Graph Neural Networks with Weak Information
Author:
Affiliation:
1. Monash University, Melbourne, VIC, Australia
2. Arizona State University, Tempe, AZ, USA
3. Texas A&M University, College Station, TX, USA
4. Griffith University, Gold Coast, Australia
Funder
Amazon Research Award
Australian Research Council
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3580305.3599410
Reference71 articles.
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2. Ming Chen Zhewei Wei Zengfeng Huang Bolin Ding and Yaliang Li. 2020b. Simple and deep graph convolutional networks. In ICML. 1725--1735. Ming Chen Zhewei Wei Zengfeng Huang Bolin Ding and Yaliang Li. 2020b. Simple and deep graph convolutional networks. In ICML. 1725--1735.
3. Xu Chen , Siheng Chen , Jiangchao Yao , Huangjie Zheng , Ya Zhang , and Ivor W Tsang . 2020a. Learning on attribute-missing graphs . IEEE TPAMI ( 2020 ). Xu Chen, Siheng Chen, Jiangchao Yao, Huangjie Zheng, Ya Zhang, and Ivor W Tsang. 2020a. Learning on attribute-missing graphs. IEEE TPAMI (2020).
4. Iterative deep graph learning for graph neural networks: Better and robust node embeddings;Chen Yu;NeurIPS,2020
5. Cluster-GCN
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