Graphs Identified by Logics with Counting
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Abstract (arXiv)
We classify graphs and, more generally, finite relational structures that are identified by C2, that is, two-variable first-order logic with counting. Using this classification, we show that it can be decided in almost linear time whether a structure is identified by C2. Our classification implies that for every graph identified by this logic, all vertex-colored versions of it are also identified. A similar statement is true for finite relational structures. We provide constructions that solve the inversion problem for finite structures in linear time. This problem has previously been shown to be polynomial time solvable by Martin Otto. For graphs, we conclude that every C2-equivalence class contains a graph whose orbits are exactly the classes of the C2-partition of its vertex set and which has a single automorphism witnessing this fact. For general k, we show that such statements are not true by providing examples of graphs of size linear in k which are identified by C3 but for which the orbit partition is strictly finer than the Ck-partition. We also provide identified graphs which have vertex-colored versions that are not identified by Ck.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Mar 2015.
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Citing papers per year: 2021: 3; 2022: 3; 2023: 3; 2024: 1; 2025: 1.
11 papers.
- 2021Weisfeiler and Leman go Machine Learning: The Story so farcited by 121
- 2021Reconstruction for Powerful Graph Representationscited by 65
- 2022Theory of Graph Neural Networks: Representation and Learningcited by 60
- 2022SpeqNets: Sparsity-aware Permutation-equivariant Graph Networkscited by 37
- 2023WL meet VCcited by 30
- 2022Weisfeiler and Leman Go Relationalcited by 28
- 2023Fine-grained Expressivity of Graph Neural Networkscited by 25
- 2021The Power of the Weisfeiler-Leman Algorithm for Machine Learning with Graphscited by 19
- 2024Bridging Weighted First Order Model Counting and Graph Polynomialscited by 5
- 2025Repetition Makes Perfect: Recurrent Graph Neural Networks Match Message-Passing Limitcited by 2
- 2023Local Fragments, Global Gains: Subgraph Counting using Graph Neural Networkscited by 0
- 2025Repetition Makes Perfect: Recurrent Graph Neural Networks Match Message-Passing Limit1 May 2025
- 2024Bridging Weighted First Order Model Counting and Graph Polynomials16 Jul 2024
- 2023Fine-grained Expressivity of Graph Neural Networks6 Jun 2023
- 2023Local Fragments, Global Gains: Subgraph Counting using Graph Neural Networks31 May 2023
- 2023WL meet VC26 Jan 2023
- 2022Weisfeiler and Leman Go Relational30 Nov 2022
- 2022Theory of Graph Neural Networks: Representation and Learning16 Apr 2022
- 2022SpeqNets: Sparsity-aware Permutation-equivariant Graph Networks25 Mar 2022
- 2021Weisfeiler and Leman go Machine Learning: The Story so far18 Dec 2021
- 2021Reconstruction for Powerful Graph Representations1 Oct 2021
- 2021The Power of the Weisfeiler-Leman Algorithm for Machine Learning with Graphs12 May 2021