Construct · 1 fields · 1 citations

Weisfeiler-Lehman bound on GNN expressiveness: a standalone construct

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Machine learning

message-passing graph neural networks can distinguish no more graph structures than the one-dimensional Weisfeiler-Lehman isomorphism test.

In machine learning, Weisfeiler-Lehman bound on GNN expressiveness refers to message-passing graph neural networks can distinguish no more graph structures than the one-dimensional Weisfeiler-Lehman isomorphism test. This meaning comes from Xu (2018) in arXiv.[1]

Unit
Recovery means
Measured by

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Nearby constructs

graph isomorphism network1 fields · 1 citationsanchor · a standalone construct isomorphism4 fields · 4 citationsjingle · one word, sameness in four meanings learning6 fields · 14 citationsjingle · one name, six constructs

References

Xu, K., Hu, W., Leskovec, J., & Jegelka, S. (2018). How Powerful are Graph Neural Networks? arXiv. https://doi.org/10.48550/arxiv.1810.00826

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