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It has been mathematically proven that GNNs are a weak form of the Weisfeiler–Lehman graph isomorphism test<ref>{{Cite journal|last=Douglas|first=B. L.|date=2011-01-27|title=The Weisfeiler–Lehman Method and Graph Isomorphism Testing|url=http://arxiv.org/abs/1101.5211|journal=arXiv:1101.5211 [math]}}</ref>, so any GNN model is at least as powerful as this test<ref>{{Cite journal|last=Xu|first=Keyulu|last2=Hu|first2=Weihua|last3=Leskovec|first3=Jure|last4=Jegelka|first4=Stefanie|date=2019-02-22|title=How Powerful are Graph Neural Networks?|url=http://arxiv.org/abs/1810.00826|journal=International Conference on Learning Representations|volume=7}}</ref>. There is now growing interest in uniting GNNs with other so-called "geometric deep learning models"<ref>{{Cite journal|last=Bronstein|first=Michael M.|last2=Bruna|first2=Joan|last3=LeCun|first3=Yann|last4=Szlam|first4=Arthur|last5=Vandergheynst|first5=Pierre|date=2017|title=Geometric Deep Learning: Going beyond Euclidean data|url=https://ieeexplore.ieee.org/document/7974879/|journal=IEEE Signal Processing Magazine|volume=34|issue=4|pages=18–42|doi=10.1109/MSP.2017.2693418|issn=1053-5888}}</ref> to better understand how and why these models work.
==References==
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