Scaling Graph Neural Networks with Approximate PageRank
Author:
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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