Faster algorithms for counting subgraphs in sparse graphs

TL;DR

This paper introduces faster algorithms for counting subgraphs in sparse graphs by leveraging a novel tree-like decomposition, significantly improving efficiency over previous methods for graphs with bounded degeneracy.

Contribution

The authors develop a new tree-like decomposition for directed acyclic graphs and a dynamic programming approach that exploits graph sparsity to improve subgraph counting algorithms.

Findings

Achieves subgraph counting in time $O(n^{0.25k + 2})$ for bounded degeneracy graphs.

Provides lower bounds based on ETH, characterizing the problem's complexity.

Generalizes bounds for counting various subgraphs like cliques and bipartite graphs.

Abstract

Given a $k$-node pattern graph $H$ and an $n$-node host graph $G$, the subgraph counting problem asks to compute the number of copies of $H$ in $G$. In this work we address the following question: can we count the copies of $H$ faster if $G$ is sparse? We answer in the affirmative by introducing a novel tree-like decomposition for directed acyclic graphs, inspired by the classic tree decomposition for undirected graphs. This decomposition gives a dynamic program for counting the homomorphisms of $H$ in $G$ by exploiting the degeneracy of $G$, which allows us to beat the state-of-the-art subgraph counting algorithms when $G$ is sparse enough. For example, we can count the induced copies of any $k$-node pattern $H$ in time $2^{O(k^2)} O(n^{0.25k + 2} \log n)$ if $G$ has bounded degeneracy, and in time $2^{O(k^2)} O(n^{0.625k + 1} \log n)$ if $G$ has bounded average degree. These bounds are instantiations of a more general result, parameterized by the degeneracy of $G$ and the structure of $H$, which generalizes classic bounds on counting cliques and complete bipartite graphs. We also give lower bounds based on the Exponential Time Hypothesis, showing that our results are actually a characterization of the complexity of subgraph counting in bounded-degeneracy graphs.