Meta-GNN-Reference-Cited by-同舟云学术

Meta-GNN

Published:2019-11-03 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 28th ACM International Conference on Information and Knowledge Management
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Author:

Zhou Fan¹,Cao Chengtai¹,Zhang Kunpeng²,Trajcevski Goce³,Zhong Ting¹,Geng Ji¹

Affiliation:

1. University of Electronic Science and Technology of China, Chengdu, China

2. University of Maryland, Washington , MD, USA

3. Iowa State University, Ames, IA, USA

Funder

Office of Naval Research

National Science Foundation

National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3357384.3358106

Reference13 articles.

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2. Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for networks. In KDD. Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for networks. In KDD.

3. Will Hamilton Zhitao Ying and Jure Leskovec. 2017. Inductive representation learning on large graphs. In NIPS. Will Hamilton Zhitao Ying and Jure Leskovec. 2017. Inductive representation learning on large graphs. In NIPS.

4. Thomas N Kipf and MaxWelling. 2017. Semi-supervised classification with graph convolutional networks. In ICLR. Thomas N Kipf and MaxWelling. 2017. Semi-supervised classification with graph convolutional networks. In ICLR.

5. Qimai Li Zhichao Han and Xiao-Ming Wu. 2018. Deeper insights into graph convolutional networks for semi-supervised learning. In AAAI. Qimai Li Zhichao Han and Xiao-Ming Wu. 2018. Deeper insights into graph convolutional networks for semi-supervised learning. In AAAI.

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