Scaling Graph Neural Networks with Approximate PageRank-Reference-Cited by-同舟云学术

Scaling Graph Neural Networks with Approximate PageRank

Published:2020-07-06 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
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Author:

Bojchevski Aleksandar¹,Klicpera Johannes¹,Perozzi Bryan²,Kapoor Amol²,Blais Martin²,Rózemberczki Benedek²,Lukasik Michal³,Günnemann Stephan¹

Affiliation:

1. Technical University of Munich, Munich, Germany

2. Google Research, New York City, NY, USA

3. Google Research, Zurich, Switzerland

Funder

Deutsche Forschungsgemeinschaft

IGSSE

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3394486.3403296

Reference55 articles.

1. S. Abu-El-Haija A. Kapoor B. Perozzi and J. Lee. 2018. N-gcn: multi-scale graph convolution for semi-supervised node classification. arXiv preprint arXiv:1802.08888. S. Abu-El-Haija A. Kapoor B. Perozzi and J. Lee. 2018. N-gcn: multi-scale graph convolution for semi-supervised node classification. arXiv preprint arXiv:1802.08888.

2. Local Computation of PageRank Contributions

3. R. Andersen F. Chung and K. Lang. 2006. Local graph partitioning using pagerank vectors. FOCS 475--486. R. Andersen F. Chung and K. Lang. 2006. Local graph partitioning using pagerank vectors. FOCS 475--486.

4. P. W. Battaglia etal 2018. Relational inductive biases deep learning and graph networks. arXiv preprint arXiv:1806.01261. P. W. Battaglia et al. 2018. Relational inductive biases deep learning and graph networks. arXiv preprint arXiv:1806.01261.

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