Designing the Topology of Graph Neural Networks: A Novel Feature Fusion Perspective-Reference-Cited by-同舟云学术

Designing the Topology of Graph Neural Networks: A Novel Feature Fusion Perspective

Published:2022-04-25 Issue: Volume: Page:
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Container-title:Proceedings of the ACM Web Conference 2022
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Author:

Wei Lanning¹,Zhao Huan²,He Zhiqiang³

Affiliation:

1. Institute of Computing Technology, Chinese Academy of Sciences, China and University of Chinese Academy of Sciences, China

2. 4Paradigm. Inc, China

3. Institute of Computing Technology, Chinese Academy of Sciences, China and Lenovo, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3485447.3512185

Reference60 articles.

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2. Aleksandar Bojchevski and Stephan Günnemann. 2018. Deep gaussian embedding of graphs: Unsupervised inductive learning via ranking. ICLR. Aleksandar Bojchevski and Stephan Günnemann. 2018. Deep gaussian embedding of graphs: Unsupervised inductive learning via ranking. ICLR.

3. Shaofei Cai , Liang Li , Jincan Deng , Beichen Zhang , Zheng-Jun Zha , Li Su , and Qingming Huang . 2021. Rethinking Graph Neural Network Search from Message-passing. CVPR ( 2021 ). Shaofei Cai, Liang Li, Jincan Deng, Beichen Zhang, Zheng-Jun Zha, Li Su, and Qingming Huang. 2021. Rethinking Graph Neural Network Search from Message-passing. CVPR (2021).

4. Deli Chen Yankai Lin Wei Li Peng Li Jie Zhou and Xu Sun. 2020. Measuring and relieving the over-smoothing problem for graph neural networks from the topological view. In AAAI Vol. 34. 3438–3445. Deli Chen Yankai Lin Wei Li Peng Li Jie Zhou and Xu Sun. 2020. Measuring and relieving the over-smoothing problem for graph neural networks from the topological view. In AAAI Vol. 34. 3438–3445.

5. Jiamin Chen Jianliang Gao Yibo Chen Moctard Babatounde Oloulade Tengfei Lyu and Zhao Li. 2021. GraphPAS: Parallel Architecture Search for Graph Neural Networks. In SIGIR. 2182–2186. Jiamin Chen Jianliang Gao Yibo Chen Moctard Babatounde Oloulade Tengfei Lyu and Zhao Li. 2021. GraphPAS: Parallel Architecture Search for Graph Neural Networks. In SIGIR. 2182–2186.

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