Homomorphisms Are a Good Basis for Counting Small Subgraphs
Not on the map. Why?
Abstract (arXiv)
We introduce graph motif parameters, a class of graph parameters that depend only on the frequencies of constant-size induced subgraphs. Classical works by Lov'asz show that many interesting quantities have this form, including, for fixed graphs H, the number of H-copies (induced or not) in an input graph G, and the number of homomorphisms from H to G. Using the framework of graph motif parameters, we obtain faster algorithms for counting subgraph copies of fixed graphs H in host graphs G: For graphs H on k edges, we show how to count subgraph copies of H in time kO(k)· n0.174k + o(k) by a surprisingly simple algorithm. This improves upon previously known running times, such as O(n0.91k + c) time for k-edge matchings or O(n0.46k + c) time for k-cycles. Furthermore, we prove a general complexity dichotomy for evaluating graph motif parameters: Given a class mathcal C of such parameters, we consider the problem of evaluating f∈ mathcal C on input graphs G, parameterized by the number of induced subgraphs that f depends upon. For every recursively enumerable class mathcal C, we prove the above problem to be either FPT or #W[1]-hard, with an explicit dichotomy criterion. This allows us to recover known dichotomies for counting subgraphs, induced subgraphs, and homomorphisms in a uniform and simplified way, together with improved lower bounds. Finally, we extend graph motif parameters to colored subgraphs and prove a complexity trichotomy: For vertex-colored graphs H and G, where H is from a fixed class mathcal H, we want to count color-preserving H-copies in G. We show that this problem is either polynomial-time solvable or FPT or #W[1]-hard, and that the FPT cases indeed need FPT time under reasonable assumptions.
Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 May 2017.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 1; 2022: 1; 2023: 4; 2024: 5; 2025: 2; 2026: 1.
14 papers.
Sort:
- 2022Expressiveness and Approximation Properties of Graph Neural Networkscited by 63
- 2021Graph Neural Networks with Local Graph Parameterscited by 51
- 2024Homomorphism Counts for Graph Neural Networks: All About That Basiscited by 16
- 2023Expectation-Complete Graph Representations with Homomorphismscited by 10
- 2023On the Power of the Weisfeiler-Leman Test for Graph Motif Parameterscited by 6
- 2024Homomorphism Counts as Structural Encodings for Graph Learningcited by 6
- 2025Homomorphism Expressivity of Spectral Invariant Graph Neural Networkscited by 4
- 2023Structural Node Embeddings with Homomorphism Countscited by 3
- 2024Weisfeiler and Leman Go Loopy: A New Hierarchy for Graph Representational Learningcited by 2
- 2025Graph Homomorphism Distortion: A Metric to Distinguish Them All and in the Latent Space Bind Themcited by 1
- 2023Local Fragments, Global Gains: Subgraph Counting using Graph Neural Networkscited by 0
- 2024Generalization of Graph Neural Networks through the Lens of Homomorphismcited by 0
2 more
- 2026Differentially Private Range Subgraph Counting6 Jun 2026
- 2025Graph Homomorphism Distortion: A Metric to Distinguish Them All and in the Latent Space Bind Them4 Nov 2025
- 2025Homomorphism Expressivity of Spectral Invariant Graph Neural Networks1 Mar 2025
- 2024Homomorphism Counts as Structural Encodings for Graph Learning24 Oct 2024
- 2024Fine-Grained Expressive Power of Weisfeiler-Leman: A Homomorphism Counting Perspective4 Oct 2024
- 2024Weisfeiler and Leman Go Loopy: A New Hierarchy for Graph Representational Learning20 Mar 2024
- 2024Generalization of Graph Neural Networks through the Lens of Homomorphism10 Mar 2024
- 2024Homomorphism Counts for Graph Neural Networks: All About That Basis13 Feb 2024
- 2023On the Power of the Weisfeiler-Leman Test for Graph Motif Parameters29 Sep 2023
- 2023Structural Node Embeddings with Homomorphism Counts29 Aug 2023
- 2023Expectation-Complete Graph Representations with Homomorphisms9 Jun 2023
- 2023Local Fragments, Global Gains: Subgraph Counting using Graph Neural Networks31 May 2023
2 more
- 2022Expressiveness and Approximation Properties of Graph Neural Networks10 Apr 2022
- 2021Graph Neural Networks with Local Graph Parameters12 Jun 2021
- 2022Expressiveness and Approximation Properties of Graph Neural Networkscited by 63
- 2021Graph Neural Networks with Local Graph Parameterscited by 51
- 2024Homomorphism Counts for Graph Neural Networks: All About That Basiscited by 16
- 2023Expectation-Complete Graph Representations with Homomorphismscited by 10
- 2023On the Power of the Weisfeiler-Leman Test for Graph Motif Parameterscited by 6
- 2024Homomorphism Counts as Structural Encodings for Graph Learningcited by 6
- 2025Homomorphism Expressivity of Spectral Invariant Graph Neural Networkscited by 4
- 2023Structural Node Embeddings with Homomorphism Countscited by 3
- 2024Weisfeiler and Leman Go Loopy: A New Hierarchy for Graph Representational Learningcited by 2
- 2025Graph Homomorphism Distortion: A Metric to Distinguish Them All and in the Latent Space Bind Themcited by 1
- 2023Local Fragments, Global Gains: Subgraph Counting using Graph Neural Networkscited by 0
- 2024Generalization of Graph Neural Networks through the Lens of Homomorphismcited by 0
12 most cited of 14 All 14 (JSON)
- 2026Differentially Private Range Subgraph Counting6 Jun 2026
- 2025Graph Homomorphism Distortion: A Metric to Distinguish Them All and in the Latent Space Bind Them4 Nov 2025
- 2025Homomorphism Expressivity of Spectral Invariant Graph Neural Networks1 Mar 2025
- 2024Homomorphism Counts as Structural Encodings for Graph Learning24 Oct 2024
- 2024Fine-Grained Expressive Power of Weisfeiler-Leman: A Homomorphism Counting Perspective4 Oct 2024
- 2024Weisfeiler and Leman Go Loopy: A New Hierarchy for Graph Representational Learning20 Mar 2024
- 2024Generalization of Graph Neural Networks through the Lens of Homomorphism10 Mar 2024
- 2024Homomorphism Counts for Graph Neural Networks: All About That Basis13 Feb 2024
- 2023On the Power of the Weisfeiler-Leman Test for Graph Motif Parameters29 Sep 2023
- 2023Structural Node Embeddings with Homomorphism Counts29 Aug 2023
- 2023Expectation-Complete Graph Representations with Homomorphisms9 Jun 2023
- 2023Local Fragments, Global Gains: Subgraph Counting using Graph Neural Networks31 May 2023
12 newest of 14 All 14 (JSON)