Backpropagation-free Graph Neural Networks-Reference-Cited by-同舟云学术

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Backpropagation-free Graph Neural Networks

Published:2022-11 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE International Conference on Data Mining (ICDM)
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Author:

Pasa Luca¹,Navarin Nicolo¹,Erb Wolfgang¹,Sperduti Alessandro¹

Affiliation:

1. University of Padova,Department of Mathematics,Padova,Italy

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10027565/10027596/10027635.pdf?arnumber=10027635

Reference34 articles.

1. Semi-Supervised Classification with Graph Convolutional Networks;kipf;ICLRE,2017

2. Neural Network for Graphs: A Contextual Constructive Approach

3. Density-preserving data visualization unveils dynamic patterns of single-cell transcriptomic variability;narayan;BioRxiv,2020

4. Inductive representation learning on large graphs;hamilton;NIPS,2017

5. Convolutional neural networks on graphs with fast localized spectral filtering;defferrard;NIPS,2016

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A unified framework for backpropagation-free soft and hard gated graph neural networks;Knowledge and Information Systems;2023-12-26

2. Towards Unsupervised Graph Completion Learning on Graphs with Features and Structure Missing;2023 IEEE International Conference on Data Mining (ICDM);2023-12-01

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