Cluster-GCN-Reference-Cited by-同舟云学术

Cluster-GCN

Published:2019-07-25 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
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Author:

Chiang Wei-Lin¹,Liu Xuanqing²,Si Si³,Li Yang³,Bengio Samy³,Hsieh Cho-Jui⁴

Affiliation:

1. National Taiwan University & Google Research, Taipei, Taiwan Roc

2. University of California, Los Angeles & Google Research, Los Angeles, CA, USA

3. Google Research, Mountain View, CA, USA

4. University of California, Los Angeles, Los Angeles, CA, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3292500.3330925

Reference17 articles.

1. Jie Chen Tengfei Ma and Cao Xiao. 2018. FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling. In ICLR. Jie Chen Tengfei Ma and Cao Xiao. 2018. FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling. In ICLR.

2. Jianfei Chen Jun Zhu and Song Le. 2018. Stochastic Training of Graph Convolutional Networks with Variance Reduction. In ICML. Jianfei Chen Jun Zhu and Song Le. 2018. Stochastic Training of Graph Convolutional Networks with Variance Reduction. In ICML.

3. Hanjun Dai Zornitsa Kozareva Bo Dai Alex Smola and Le Song. 2018. Learning Steady-States of Iterative Algorithms over Graphs. In ICML. 1114--1122. Hanjun Dai Zornitsa Kozareva Bo Dai Alex Smola and Le Song. 2018. Learning Steady-States of Iterative Algorithms over Graphs. In ICML. 1114--1122.

4. Weighted Graph Cuts without Eigenvectors A Multilevel Approach

5. William L. Hamilton Rex Ying and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. In NIPS. William L. Hamilton Rex Ying and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. In NIPS.

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