12 research outputs found
Counting induced subgraphs: a topological approach to #W[1]-hardness
We investigate the problem of counting all induced subgraphs of size in a graph that satisfy a given property . This continues the work of Jerrum and Meeks who proved the problem to be -hard for some families of properties which include, among others, (dis)connectedness [JCSS 15] and even- or oddness of the number of edges [Combinatorica 17]. Using the recent framework of graph motif parameters due to Curticapean, Dell and Marx [STOC 17], we discover that for monotone properties , the problem is hard for if the reduced Euler characteristic of the associated simplicial (graph) complex of is non-zero. This observation links to Karp's famous Evasiveness Conjecture, as every graph complex with non-vanishing reduced Euler characteristic is known to be evasive. Applying tools from the "topological approach to evasiveness" which was introduced in the seminal paper of Khan, Saks and Sturtevant [FOCS 83], we prove that is -hard for every monotone property that does not hold on the Hamilton cycle as well as for some monotone properties that hold on the Hamilton cycle such as being triangle-free or not -edge-connected for . Moreover, we show that for those properties can not be solved in time for any computable function unless the Exponential Time Hypothesis (ETH) fails. In the final part of the paper, we investigate non-monotone properties and prove that is -hard if is any non-trivial modularity constraint on the number of edges with respect to some prime or if enforces the presence of a fixed isolated subgraph
Counting small induced subgraphs satisfying monotone properties
Given a graph property , the problem asks, on input a graph and a positive integer , to compute the number of induced subgraphs of size in that satisfy . The search for explicit criteria on ensuring that is hard was initiated by Jerrum and Meeks [J. Comput. Syst. Sci. 15] and is part of the major line of research on counting small patterns in graphs. However, apart from an implicit result due to Curticapean, Dell and Marx [STOC 17] proving that a full classification into "easy" and "hard" properties is possible and some partial results on edge-monotone properties due to Meeks [Discret. Appl. Math. 16] and D\"orfler et al. [MFCS 19], not much is known. In this work, we fully answer and explicitly classify the case of monotone, that is subgraph-closed, properties: We show that for any non-trivial monotone property , the problem cannot be solved in time for any function , unless the Exponential Time Hypothesis fails. By this, we establish that any significant improvement over the brute-force approach is unlikely; in the language of parameterized complexity, we also obtain a -completeness result
Counting small induced subgraphs satisfying monotone properties
Given a graph property , the problem asks, on input a graph and a positive integer , to compute the number of induced subgraphs of size in that satisfy . The search for explicit criteria on ensuring that is hard was initiated by Jerrum and Meeks [J. Comput. Syst. Sci. 15] and is part of the major line of research on counting small patterns in graphs. However, apart from an implicit result due to Curticapean, Dell and Marx [STOC 17] proving that a full classification into "easy" and "hard" properties is possible and some partial results on edge-monotone properties due to Meeks [Discret. Appl. Math. 16] and D\"orfler et al. [MFCS 19], not much is known. In this work, we fully answer and explicitly classify the case of monotone, that is subgraph-closed, properties: We show that for any non-trivial monotone property , the problem cannot be solved in time for any function , unless the Exponential Time Hypothesis fails. By this, we establish that any significant improvement over the brute-force approach is unlikely; in the language of parameterized complexity, we also obtain a -completeness result
Counting Small Induced Subgraphs Satisfying Monotone Properties
Given a graph property , the problem asks, on input a graph and a positive integer , to compute the number of induced subgraphs of size in that satisfy . The search for explicit criteria on ensuring that is hard was initiated by Jerrum and Meeks [J. Comput. Syst. Sci. 15] and is part of the major line of research on counting small patterns in graphs. However, apart from an implicit result due to Curticapean, Dell and Marx [STOC 17] proving that a full classification into "easy" and "hard" properties is possible and some partial results on edge-monotone properties due to Meeks [Discret. Appl. Math. 16] and D\"orfler et al. [MFCS 19], not much is known. In this work, we fully answer and explicitly classify the case of monotone, that is subgraph-closed, properties: We show that for any non-trivial monotone property , the problem cannot be solved in time for any function , unless the Exponential Time Hypothesis fails. By this, we establish that any significant improvement over the brute-force approach is unlikely; in the language of parameterized complexity, we also obtain a -completeness result
Counting Small Induced Subgraphs Satisfying Monotone Properties
Given a graph property , the problem asks, on
input a graph and a positive integer , to compute the number of induced
subgraphs of size in that satisfy . The search for explicit
criteria on ensuring that is hard was
initiated by Jerrum and Meeks [J. Comput. Syst. Sci. 15] and is part of the
major line of research on counting small patterns in graphs. However, apart
from an implicit result due to Curticapean, Dell and Marx [STOC 17] proving
that a full classification into "easy" and "hard" properties is possible and
some partial results on edge-monotone properties due to Meeks [Discret. Appl.
Math. 16] and D\"orfler et al. [MFCS 19], not much is known.
In this work, we fully answer and explicitly classify the case of monotone,
that is subgraph-closed, properties: We show that for any non-trivial monotone
property , the problem cannot be solved in time
for any function , unless the
Exponential Time Hypothesis fails. By this, we establish that any significant
improvement over the brute-force approach is unlikely; in the language of
parameterized complexity, we also obtain a -completeness
result.Comment: 33 pages, 2 figure
Homomorphisms are a good basis for counting small subgraphs
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 parameters: Given a class of such parameters, we consider
the problem of evaluating on input graphs , parameterized
by the number of induced subgraphs that depends upon. For every recursively
enumerable class , 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 and , where is from
a fixed class , we want to count color-preserving -copies in
. 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.Comment: An extended abstract of this paper appears at STOC 201
Counting Small Induced Subgraphs with Edge-monotone Properties
We study the parameterized complexity of #IndSub(), where given a graph
and an integer , the task is to count the number of induced subgraphs on
vertices that satisfy the graph property . Focke and Roth [STOC 2022]
completely characterized the complexity for each that is a hereditary
property (that is, closed under vertex deletions): #IndSub() is
#W[1]-hard except in the degenerate cases when every graph satisfies or
only finitely many graphs satisfy . We complement this result with a
classification for each that is edge monotone (that is, closed under
edge deletions): #IndSub() is #W[1]-hard except in the degenerate case
when there are only finitely many integers such that is nontrivial
on -vertex graphs. Our result generalizes earlier results for specific
properties that are related to the connectivity or density of the graph.
Further, we extend the #W[1]-hardness result by a lower bound which shows
that #IndSub() cannot be solved in time for any function , unless the Exponential-Time Hypothesis
(ETH) fails. For many natural properties, we obtain even a tight bound ; for example, this is the case for every property
that is nontrivial on -vertex graphs for each greater than some
Counting Small Induced Subgraphs with Hereditary Properties
We study the computational complexity of the problem #IndSub(\Phi) of counting k-vertex induced subgraphs of a graph G that satisfy a graph property \Phi. Our main result establishes an exhaustive and explicit classification for all hereditary properties, including tight conditional lower bounds under the Exponential Time Hypothesis (ETH):
- If a hereditary property \Phi is true for all graphs, or if it is true only for finitely many graphs, then #IndSub(\Phi) is solvable in polynomial time.
- Otherwise, #IndSub(\Phi) is #W[1]-complete when parameterised by k, and, assuming ETH, it cannot be solved in time f(k)*|G|^{o(k)} for any function f.
This classification features a wide range of properties for which the corresponding detection problem (as classified by Khot and Raman [TCS 02]) is tractable but counting is hard. Moreover, even for properties which are already intractable in their decision version, our results yield significantly stronger lower bounds for the counting problem.
As additional result, we also present an exhaustive and explicit parameterised complexity classification for all properties that are invariant under homomorphic equivalence.
By covering one of the most natural and general notions of closure, namely, closure under vertex-deletion (hereditary), we generalise some of the earlier results on this problem. For instance, our results fully subsume and strengthen the existing classification of #IndSub(\Phi) for monotone (subgraph-closed) properties due to Roth, Schmitt, and Wellnitz [FOCS 20]