PaSca: A Graph Neural Architecture Search System under the Scalable Paradigm-Reference-Cited by-同舟云学术

PaSca: A Graph Neural Architecture Search System under the Scalable Paradigm

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

Zhang Wentao¹,Shen Yu²,Lin Zheyu²,Li Yang²,Li Xiaosen³,Ouyang Wen³,Tao Yangyu³,Yang Zhi²,Cui Bin⁴

Affiliation:

1. School of CS & Key Lab of High Confidence Software Technologies, Peking University, China and Tencent Inc., China

2. School of CS & Key Lab of High Confidence Software Technologies, Peking University, China

3. Tencent Inc., China

4. School of CS & Key Lab of High Confidence Software Technologies, Peking University, China and Institute of Computational Social Science, Peking University (Qingdao), China

Funder

NSFC

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3485447.3511986

Reference68 articles.

1. RECON: Relation Extraction using Knowledge Graph Context in a Graph Neural Network

2. Geometric Deep Learning: Going beyond Euclidean data

3. A Multi-Scale Approach for Graph Link Prediction

4. Rethinking Graph Neural Architecture Search from Message-passing

5. Jie Chen , Tengfei Ma , and Cao Xiao . 2018 . FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling . In International Conference on Learning Representations. Jie Chen, Tengfei Ma, and Cao Xiao. 2018. FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling. In International Conference on Learning Representations.

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