NeurIPS 2019poster345 citations
On the equivalence between graph isomorphism testing and function approximation with GNNs
Zhengdao Chen, Soledad Villar, Lei Chen, Joan Bruna
Abstract
Graph neural networks (GNNs) have achieved lots of success on graph-structured data. In light of this, there has been increasing interest in studying their representation power. One line of work focuses on the universal approximation of permutation-invariant functions by certain classes of GNNs, and another demonstrates the limitation of GNNs via graph isomorphism tests.
BibTeX
@inproceedings{NEURIPS2019_71ee911d,
author = {Chen, Zhengdao and Villar, Soledad and Chen, Lei and Bruna, Joan},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {On the equivalence between graph isomorphism testing and function approximation with GNNs},
url = {https://proceedings.neurips.cc/paper_files/paper/2019/file/71ee911dd06428a96c143a0b135041a4-Paper.pdf},
volume = {32},
year = {2019}
}