How statement and proof provenance work
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Finite-round one-dimensional Weisfeiler–Leman color refinement
Definition
Let and be finite vertex-labeled simple undirected graphs with labels in a common set . Define color sets recursively by and for each integer . Set and ; for , update
The graph color multiset at round is , and likewise for . One-dimensional Weisfeiler–Leman color refinement distinguishes and at round when ; it distinguishes the pair when this holds for some finite . If no finite round distinguishes them, they are one-WL-indistinguishable. In the unlabeled case, take to be a singleton. The tuple update is the canonical collision-free color encoding.
Definition references
finite vertex-labeled simple undirected graphs and neighborhoods
finite multisets and color multiplicities
Xu, Hu, Leskovec, and Jegelka, “How Powerful are Graph Neural Networks?”, §2
Morris et al., “Weisfeiler and Leman Go Neural,” §2, Eq. (1)
Li and Leskovec, Graph Representation Learning, Chapter 5 §5.3.3
Depends on
Used by
- A six-cycle and two disjoint triangles are regular but one-WL-indistinguishable Example
- One-dimensional Weisfeiler–Leman refinement is invariant under graph isomorphism Lemma
- Injective message passing matches the one-dimensional Weisfeiler–Leman test Theorem
- Message-passing GNNs cannot distinguish beyond one-dimensional Weisfeiler–Leman Theorem
Dependency tree · one level
2 results within one dependency step of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.
Sources
- Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka, How Powerful are Graph Neural Networks? (standard reference, not scraped)
- Christopher Morris et al., Weisfeiler and Leman Go Neural: Higher-Order Graph Neural Networks (standard reference, not scraped)
- Pan Li and Jure Leskovec, Graph Representation Learning (standard reference, not scraped)