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Counting Small Induced Subgraphs: Hardness via Fourier Analysis

Radu Curticapean, Daniel Neuen

TL;DR

The paper studies counting induced k-vertex subgraphs that satisfy a fixed graph property Phi, and proves ETH-based lower bounds on the running time exponent for brute-force counting via an algebraic framework. It introduces a sub-expansion technique that expresses counts as linear combinations of subgraph counts, and then analyzes polynomial representations and alternating enumerators to derive hardness. The results span sparse/hereditary, edge-monotone, fully symmetric, weight-avoiding, small-image, and All-Even properties, yielding #W[1]-hardness and tight ETH lower bounds, along with Weisfeiler-Leman dimension bounds. The approach unifies prior combinatorial, group-theoretic, and topological methods into a streamlined algebraic analysis, with implications for modular counting and parameterized complexity in induced-subgraph counting problems.

Abstract

For a fixed graph property $Φ$ and integer $k \geq 1$, consider the problem of counting the induced $k$-vertex subgraphs satisfying $Φ$ in an input graph $G$. This problem can be solved by brute-force in time $O(n^{k})$. Under ETH, we prove several lower bounds on the optimal exponent in this running time: If $Φ$ is edge-monotone (i.e., closed under deleting edges), then ETH rules out $n^{o(k)}$ time algorithms for this problem. This strengthens a recent lower bound by Döring, Marx and Wellnitz [STOC 2024]. Our result also holds for counting modulo fixed primes. If at most $(2-\varepsilon)^{\binom{k}{2}}$ graphs on $k$ vertices satisfy $Φ$, for some $\varepsilon > 0$, then ETH also rules out an exponent of $o(k)$. This holds even when the graphs in $Φ$ have arbitrary individual weights, generalizing previous results for hereditary properties by Focke and Roth [SIAM J. Comput. 2024]. If $Φ$ is non-trivial and excludes $β_Φ$ edge-densities, then the optimal exponent under ETH is $Ω(β_Φ)$. This holds even when the graphs in $Φ$ have arbitrary individual weights, generalizing previous results by Roth, Schmitt and Wellnitz [SIAM J. Comput. 2024]. In all cases, we also obtain $\mathsf{\#W[1]}$-hardness if $k$ is part of the input and considered as the parameter. We also obtain lower bounds on the Weisfeiler-Leman dimension. As opposed to the nontrivial techniques from combinatorics, group theory, and simplicial topology used before, our results follow from a relatively straightforward ``algebraization'' of the problem in terms of polynomials, combined with applications of simple algebraic facts, which can also be interpreted in terms of Fourier analysis.

Counting Small Induced Subgraphs: Hardness via Fourier Analysis

TL;DR

The paper studies counting induced k-vertex subgraphs that satisfy a fixed graph property Phi, and proves ETH-based lower bounds on the running time exponent for brute-force counting via an algebraic framework. It introduces a sub-expansion technique that expresses counts as linear combinations of subgraph counts, and then analyzes polynomial representations and alternating enumerators to derive hardness. The results span sparse/hereditary, edge-monotone, fully symmetric, weight-avoiding, small-image, and All-Even properties, yielding #W[1]-hardness and tight ETH lower bounds, along with Weisfeiler-Leman dimension bounds. The approach unifies prior combinatorial, group-theoretic, and topological methods into a streamlined algebraic analysis, with implications for modular counting and parameterized complexity in induced-subgraph counting problems.

Abstract

For a fixed graph property and integer , consider the problem of counting the induced -vertex subgraphs satisfying in an input graph . This problem can be solved by brute-force in time . Under ETH, we prove several lower bounds on the optimal exponent in this running time: If is edge-monotone (i.e., closed under deleting edges), then ETH rules out time algorithms for this problem. This strengthens a recent lower bound by Döring, Marx and Wellnitz [STOC 2024]. Our result also holds for counting modulo fixed primes. If at most graphs on vertices satisfy , for some , then ETH also rules out an exponent of . This holds even when the graphs in have arbitrary individual weights, generalizing previous results for hereditary properties by Focke and Roth [SIAM J. Comput. 2024]. If is non-trivial and excludes edge-densities, then the optimal exponent under ETH is . This holds even when the graphs in have arbitrary individual weights, generalizing previous results by Roth, Schmitt and Wellnitz [SIAM J. Comput. 2024]. In all cases, we also obtain -hardness if is part of the input and considered as the parameter. We also obtain lower bounds on the Weisfeiler-Leman dimension. As opposed to the nontrivial techniques from combinatorics, group theory, and simplicial topology used before, our results follow from a relatively straightforward ``algebraization'' of the problem in terms of polynomials, combined with applications of simple algebraic facts, which can also be interpreted in terms of Fourier analysis.
Paper Structure (44 sections, 50 theorems, 102 equations, 1 figure)

This paper contains 44 sections, 50 theorems, 102 equations, 1 figure.

Key Result

Theorem 1.1

Assuming ETH, there is a constant $\beta > 0$ such that, for every pattern $H$ with $k$ vertices and $\ell$ edges, colorful $H$-subgraph copies in $n$-vertex cannot be counted in $O(n^{\beta \cdot \ell/k})$ time.

Figures (1)

  • Figure 1: Visualization of the construction of the graph $H^*$ in the proof of Lemma \ref{['lem:alternating-enumerator-monotone-prime-power']} for $p = 3$. Each thick gray edge represents a complete biclique between the corresponding sets.

Theorems & Definitions (109)

  • Theorem 1.1: CurticapeanDNW25
  • Theorem 1.2
  • Theorem 1.3
  • Theorem 1.4
  • Theorem 1.5
  • Theorem 1.6
  • Conjecture 2.1: Exponential Time Hypothesis (ETH)
  • Conjecture 2.2: Randomized Exponential Time Hypothesis (rETH)
  • Lemma 2.3: Uncertainty Principle
  • Definition 3.1
  • ...and 99 more