TL;DR
This paper introduces graph motif parameters to efficiently count small subgraphs and establishes a complexity dichotomy for evaluating these parameters, leading to faster algorithms and a unified understanding of their computational complexity.
Contribution
It presents a new framework for counting subgraphs using graph motif parameters, improves algorithmic running times, and provides a comprehensive complexity classification for evaluating these parameters.
Findings
Faster algorithms for counting subgraph copies with time $k^{O(k)} imes n^{0.174k + o(k)}$
A complexity dichotomy classifying evaluation problems as FPT or #W[1]-hard
A complexity trichotomy for colored subgraph counting problems
Abstract
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 , the number of -copies (induced or not) in an input graph , and the number of homomorphisms from to . Using the framework of graph motif parameters, we obtain faster algorithms for counting subgraph copies of fixed graphs in host graphs : For graphs on edges, we show how to count subgraph copies of in time by a surprisingly simple algorithm. This improves upon previously known running times, such as time for -edge matchings or time for -cycles. Furthermore, we prove a general complexity dichotomy for evaluating graph motif…
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