The Hardness of Subgraph Isomorphism

TL;DR

This paper establishes a near-tight exponential lower bound for the Subgraph Isomorphism problem, showing it is computationally harder than previously known problems like Clique or Hamiltonian cycles, under the Exponential Time Hypothesis.

Contribution

The authors provide a reduction from 3-SAT that proves a super-polynomial lower bound for Subgraph Isomorphism, advancing understanding of its computational complexity.

Findings

Proves a $2^{ ext{Omega}(n \sqrt{ ext{log} n})}$ lower bound under ETH.

Transfers hardness results from Graph Homomorphism to Subgraph Isomorphism.

Shows certain graph classes are strictly harder to embed than cliques or cycles.

Abstract

Subgraph Isomorphism is a very basic graph problem, where given two graphs $G$ and $H$ one is to check whether $G$ is a subgraph of $H$. Despite its simple definition, the Subgraph Isomorphism problem turns out to be very broad, as it generalizes problems such as Clique, $r$-Coloring, Hamiltonicity, Set Packing and Bandwidth. However, for all of the mentioned problems $2^{\mathcal{O}(n)}$ time algorithms exist, so a natural and frequently asked question in the past was whether there exists a $2^{\mathcal{O}(n)}$ time algorithm for Subgraph Isomorphism. In the monograph of Fomin and Kratsch [Springer'10] this question is highlighted as an open problem, among few others. Our main result is a reduction from 3-SAT, producing a subexponential number of sublinear instances of the Subgraph Isomorphism problem. In particular, our reduction implies a $2^{\Omega(n \sqrt{\log n})}$ lower bound for Subgraph Isomorphism under the Exponential Time Hypothesis. This shows that there exist classes of graphs that are strictly harder to embed than cliques or Hamiltonian cycles. The core of our reduction consists of two steps. First, we preprocess and pack variables and clauses of a 3-SAT formula into groups of logarithmic size. However, the grouping is not arbitrary, since as a result we obtain only a limited interaction between the groups. In the second step, we overcome the technical hardness of encoding evaluations as permutations by a simple, yet fruitful scheme of guessing the sizes of preimages of an arbitrary mapping, reducing the case of arbitrary mapping to bijections. In fact, when applying this step to a recent independent result of Fomin et al.[arXiv:1502.05447 (2015)], who showed hardness of Graph Homomorphism, we can transfer their hardness result to Subgraph Isomorphism, implying a nearly tight lower bound of $2^{\Omega(n \log n / \log \log n)}$.