Graph Neural Networks are More Powerful than We Think-Reference-Cited by-同舟云学术

Graph Neural Networks are More Powerful than We Think

Published:2024-04-14 Issue: Volume: Page:
ISSN:
Container-title:ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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Author:

Kanatsoulis Charilaos I.¹,Ribeiro Alejandro¹

Affiliation:

1. University of Pennsylvania,Dept. of Electrical and Systems Engineering,Philadelphia,PA,USA

Publisher

IEEE

Link

Reference31 articles.

1. Invariant and equivariant graph networks;Maron

3. Graphon neural networks and the transferability of graph neural networks;Ruiz;Advances in Neural Information Processing Systems,2020

4. Transferability of spectral graph convolutional neural networks;Levie;Journal of Machine Learning Research,2021

5. On the universality of invariant networks;Maron

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1. A Graph Autoencoder Approach to Crowdsourcing;2024 IEEE 13rd Sensor Array and Multichannel Signal Processing Workshop (SAM);2024-07-08

4. Robust and Transferable Graph Neural Networks for Medical Images;2023 International Conference on Machine Learning and Applications (ICMLA);2023-12-15