Graph neural network: Difference between revisions

Revision as of 07:44, 7 February 2022

Revision as of 20:36, 28 February 2022

Line 4:

GNNs are a weak form of the Weisfeiler–Lehman graph isomorphism test,<ref>{{cite arXiv|last=Douglas|first=B. L.|date=2011-01-27|title=The Weisfeiler–Lehman Method and Graph Isomorphism Testing|class=math.CO|eprint=1101.5211}}</ref> so any GNN model is at least as powerful as this test.<ref>{{Cite journal|last1=Xu|first1=Keyulu|last2=Hu|first2=Weihua|last3=Leskovec|first3=Jure|last4=Jegelka|first4=Stefanie|date=2019-02-22|title=How Powerful are Graph Neural Networks?|arxiv=1810.00826|journal=International Conference on Learning Representations|volume=7}}</ref> Researcher are attempting to unite GNNs with other "geometric deep learning models"<ref>{{Cite journal|last1=Bronstein|first1=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|arxiv=1611.08097|bibcode=2017ISPM...34...18B|s2cid=15195762}}</ref> to better understand how and why these models work.

In the case of the absence of a known graph structure for example a k-[[nearest neighbor graph]] can be heuristically induced.

==References==