Counting Induced Subgraphs: An Algebraic Approach to #W[1]-Hardness

AbstractWe study the problem $$\#\textsc {IndSub}(\varPhi )$$ # I N D S U B ( Φ ) of counting all induced subgraphs of size k in a graph G that satisfy the property $$\varPhi $$ Φ . It is shown that, given any graph property $$\varPhi $$ Φ that distinguishes independent sets from bicliques, $$\#\textsc {IndSub}(\varPhi )$$ # I N D S U B ( Φ ) is hard for the class $$\#\mathsf {W[1]}$$ # W [ 1 ] , i.e., the parameterized counting equivalent of $${{\mathsf {N}}}{{\mathsf {P}}}$$ N P . Under additional suitable density conditions on $$\varPhi $$ Φ , satisfied e.g. by non-trivial monotone properties on bipartite graphs, we strengthen $$\#\mathsf {W[1]}$$ # W [ 1 ] -hardness by establishing that $$\#\textsc {IndSub}(\varPhi )$$ # I N D S U B ( Φ ) cannot be solved in time $$f(k)\cdot n^{o(k)}$$ f ( k ) · n o ( k ) for any computable function f, unless the Exponential Time Hypothesis fails. Finally, we observe that our results remain true even if the input graph G is restricted to be bipartite and counting is done modulo a fixed prime.

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A Note on the Speed of Hereditary Graph Properties

The Electronic Journal of Combinatorics ◽

10.37236/644 ◽

2011 ◽

Vol 18 (1) ◽

Cited By ~ 4

Author(s):

Vadim V. Lozin ◽

Colin Mayhill ◽

Victor Zamaraev

Keyword(s):

Practical Importance ◽

Bipartite Graphs ◽

Hereditary Property ◽

Induced Subgraphs ◽

Forbidden Induced Subgraphs ◽

Graph Properties ◽

Graph Property ◽

Vertex Set ◽

Split Graphs ◽

Hereditary Properties

For a graph property $X$, let $X_n$ be the number of graphs with vertex set $\{1,\ldots,n\}$ having property $X$, also known as the speed of $X$. A property $X$ is called factorial if $X$ is hereditary (i.e. closed under taking induced subgraphs) and $n^{c_1n}\le X_n\le n^{c_2n}$ for some positive constants $c_1$ and $c_2$. Hereditary properties with the speed slower than factorial are surprisingly well structured. The situation with factorial properties is more complicated and less explored, although this family includes many properties of theoretical or practical importance, such as planar graphs or graphs of bounded vertex degree. To simplify the study of factorial properties, we propose the following conjecture: the speed of a hereditary property $X$ is factorial if and only if the fastest of the following three properties is factorial: bipartite graphs in $X$, co-bipartite graphs in $X$ and split graphs in $X$. In this note, we verify the conjecture for hereditary properties defined by forbidden induced subgraphs with at most 4 vertices.

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Structural Parameterizations of Clique Coloring

Algorithmica ◽

10.1007/s00453-021-00890-z ◽

2021 ◽

Author(s):

Lars Jaffke ◽

Paloma T. Lima ◽

Geevarghese Philip

Keyword(s):

Lower Bound ◽

Maximal Clique ◽

Time Algorithm ◽

Input Graph ◽

Exponential Time ◽

Exponential Time Hypothesis ◽

Tree Decompositions

AbstractA clique coloring of a graph is an assignment of colors to its vertices such that no maximal clique is monochromatic. We initiate the study of structural parameterizations of the Clique Coloring problem which asks whether a given graph has a clique coloring with q colors. For fixed $$q \ge 2$$ q ≥ 2 , we give an $$\mathscr {O}^{\star }(q^{{\mathsf {tw}}})$$ O ⋆ ( q tw ) -time algorithm when the input graph is given together with one of its tree decompositions of width $${\mathsf {tw}} $$ tw . We complement this result with a matching lower bound under the Strong Exponential Time Hypothesis. We furthermore show that (when the number of colors is unbounded) Clique Coloring is $$\mathsf {XP}$$ XP parameterized by clique-width.

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Matching Cut in Graphs with Large Minimum Degree

Algorithmica ◽

10.1007/s00453-020-00782-8 ◽

2020 ◽

Author(s):

Chi-Yeh Chen ◽

Sun-Yuan Hsieh ◽

Hoang-Oanh Le ◽

Van Bang Le ◽

Sheng-Lung Peng

Keyword(s):

Polynomial Time ◽

Minimum Degree ◽

Positive Root ◽

Time Algorithm ◽

Bipartite Graphs ◽

Exponential Time ◽

Running Time ◽

Subexponential Time ◽

Exponential Time Hypothesis ◽

Exponential Time Algorithm

AbstractIn a graph, a matching cut is an edge cut that is a matching. Matching Cut is the problem of deciding whether or not a given graph has a matching cut, which is known to be $${\mathsf {NP}}$$ NP -complete. While Matching Cut is trivial for graphs with minimum degree at most one, it is $${\mathsf {NP}}$$ NP -complete on graphs with minimum degree two. In this paper, we show that, for any given constant $$c>1$$ c > 1 , Matching Cut is $${\mathsf {NP}}$$ NP -complete in the class of graphs with minimum degree c and this restriction of Matching Cut has no subexponential-time algorithm in the number of vertices unless the Exponential-Time Hypothesis fails. We also show that, for any given constant $$\epsilon >0$$ ϵ > 0 , Matching Cut remains $${\mathsf {NP}}$$ NP -complete in the class of n-vertex (bipartite) graphs with unbounded minimum degree $$\delta >n^{1-\epsilon }$$ δ > n 1 - ϵ . We give an exact branching algorithm to solve Matching Cut for graphs with minimum degree $$\delta \ge 3$$ δ ≥ 3 in $$O^*(\lambda ^n)$$ O ∗ ( λ n ) time, where $$\lambda$$ λ is the positive root of the polynomial $$x^{\delta +1}-x^{\delta }-1$$ x δ + 1 - x δ - 1 . Despite the hardness results, this is a very fast exact exponential-time algorithm for Matching Cut on graphs with large minimum degree; for instance, the running time is $$O^*(1.0099^n)$$ O ∗ ( 1 . 0099 n ) on graphs with minimum degree $$\delta \ge 469$$ δ ≥ 469 . Complementing our hardness results, we show that, for any two fixed constants $$1< c <4$$ 1 < c < 4 and $$c^{\prime }\ge 0$$ c ′ ≥ 0 , Matching Cut is solvable in polynomial time for graphs with large minimum degree $$\delta \ge \frac{1}{c}n-c^{\prime }$$ δ ≥ 1 c n - c ′ .

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Metric Dimension Parameterized By Treewidth

Algorithmica ◽

10.1007/s00453-021-00808-9 ◽

2021 ◽

Author(s):

Édouard Bonnet ◽

Nidhi Purohit

Keyword(s):

Computable Function ◽

Polynomial Time Algorithm ◽

Time Algorithm ◽

Minimum Size ◽

Metric Dimension ◽

Resolving Set ◽

Input Graph ◽

Outerplanar Graphs ◽

Bounded Treewidth ◽

Fpt Algorithm

AbstractA resolving set S of a graph G is a subset of its vertices such that no two vertices of G have the same distance vector to S. The Metric Dimension problem asks for a resolving set of minimum size, and in its decision form, a resolving set of size at most some specified integer. This problem is NP-complete, and remains so in very restricted classes of graphs. It is also W[2]-complete with respect to the size of the solution. Metric Dimension has proven elusive on graphs of bounded treewidth. On the algorithmic side, a polynomial time algorithm is known for trees, and even for outerplanar graphs, but the general case of treewidth at most two is open. On the complexity side, no parameterized hardness is known. This has led several papers on the topic to ask for the parameterized complexity of Metric Dimension with respect to treewidth. We provide a first answer to the question. We show that Metric Dimension parameterized by the treewidth of the input graph is W[1]-hard. More refinedly we prove that, unless the Exponential Time Hypothesis fails, there is no algorithm solving Metric Dimension in time $$f(\text {pw})n^{o(\text {pw})}$$ f ( pw ) n o ( pw ) on n-vertex graphs of constant degree, with $$\text {pw}$$ pw the pathwidth of the input graph, and f any computable function. This is in stark contrast with an FPT algorithm of Belmonte et al. (SIAM J Discrete Math 31(2):1217–1243, 2017) with respect to the combined parameter $$\text {tl}+\Delta$$ tl + Δ , where $$\text {tl}$$ tl is the tree-length and $$\Delta$$ Δ the maximum-degree of the input graph.

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Lower Bounds Based on the Exponential Time Hypothesis: Edge Clique Cover

Encyclopedia of Algorithms ◽

10.1007/978-1-4939-2864-4_777 ◽

2016 ◽

pp. 1159-1162

Author(s):

Michał Pilipczuk

Keyword(s):

Lower Bounds ◽

Exponential Time ◽

Exponential Time Hypothesis ◽

Edge Clique Cover

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Exact Algorithms and Strong Exponential Time Hypothesis

Encyclopedia of Algorithms ◽

10.1007/978-1-4939-2864-4_519 ◽

2016 ◽

pp. 657-661 ◽

Cited By ~ 1

Author(s):

Joshua R. Wang ◽

Ryan Williams

Keyword(s):

Exact Algorithms ◽

Exponential Time ◽

Exponential Time Hypothesis

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Matrix Partitions with Finitely Many Obstructions

The Electronic Journal of Combinatorics ◽

10.37236/976 ◽

2007 ◽

Vol 14 (1) ◽

Cited By ~ 8

Author(s):

Tomás Feder ◽

Pavol Hell ◽

Wing Xie

Keyword(s):

Symmetric Matrix ◽

Forbidden Subgraph ◽

Input Graph ◽

Induced Subgraphs ◽

Induced Subgraph ◽

Forbidden Induced Subgraphs ◽

Extra Effort ◽

Matrix Partition ◽

Finite Set ◽

Forbidden Induced Subgraph

Each $m$ by $m$ symmetric matrix $M$ over $0, 1, *$, defines a partition problem, in which an input graph $G$ is to be partitioned into $m$ parts with adjacencies governed by $M$, in the sense that two distinct vertices in (possibly equal) parts $i$ and $j$ are adjacent if $M(i,j)=1$, and nonadjacent if $M(i,j)=0$. (The entry $*$ implies no restriction.) We ask which matrix partition problems admit a characterization by a finite set of forbidden induced subgraphs. We prove that matrices containing a certain two by two diagonal submatrix $S$ never have such characterizations. We then develop a recursive technique that allows us (with some extra effort) to verify that matrices without $S$ of size five or less always have a finite forbidden induced subgraph characterization. However, we exhibit a six by six matrix without $S$ which cannot be characterized by finitely many induced subgraphs. We also explore the connection between finite forbidden subgraph characterizations and related questions on the descriptive and computational complexity of matrix partition problems.

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Results on a Super Strong Exponential Time Hypothesis

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i09.7125 ◽

2020 ◽

Vol 34 (09) ◽

pp. 13700-13703

Author(s):

Nikhil Vyas ◽

Ryan Williams

Keyword(s):

Randomized Algorithm ◽

Time Algorithm ◽

Critical Threshold ◽

Polynomial Method ◽

Variable Ratio ◽

Sat Solving ◽

Worst Case ◽

Exponential Time ◽

Exponential Time Hypothesis ◽

Special Case

All known SAT-solving paradigms (backtracking, local search, and the polynomial method) only yield a 2n(1−1/O(k)) time algorithm for solving k-SAT in the worst case, where the big-O constant is independent of k. For this reason, it has been hypothesized that k-SAT cannot be solved in worst-case 2n(1−f(k)/k) time, for any unbounded ƒ : ℕ → ℕ. This hypothesis has been called the “Super-Strong Exponential Time Hypothesis” (Super Strong ETH), modeled after the ETH and the Strong ETH. We prove two results concerning the Super-Strong ETH:1. It has also been hypothesized that k-SAT is hard to solve for randomly chosen instances near the “critical threshold”, where the clause-to-variable ratio is 2k ln 2 −Θ(1). We give a randomized algorithm which refutes the Super-Strong ETH for the case of random k-SAT and planted k-SAT for any clause-to-variable ratio. In particular, given any random k-SAT instance F with n variables and m clauses, our algorithm decides satisfiability for F in 2n(1−Ω( log k)/k) time, with high probability (over the choice of the formula and the randomness of the algorithm). It turns out that a well-known algorithm from the literature on SAT algorithms does the job: the PPZ algorithm of Paturi, Pudlak, and Zane (1998).2. The Unique k-SAT problem is the special case where there is at most one satisfying assignment. It is natural to hypothesize that the worst-case (exponential-time) complexity of Unique k-SAT is substantially less than that of k-SAT. Improving prior reductions, we show the time complexities of Unique k-SAT and k-SAT are very tightly related: if Unique k-SAT is in 2n(1−f(k)/k) time for an unbounded f, then k-SAT is in 2n(1−f(k)(1−ɛ)/k) time for every ɛ > 0. Thus, refuting Super Strong ETH in the unique solution case would refute Super Strong ETH in general.

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