343 research outputs found
Algorithmic and Hardness Results for the Colorful Components Problems
In this paper we investigate the colorful components framework, motivated by
applications emerging from comparative genomics. The general goal is to remove
a collection of edges from an undirected vertex-colored graph such that in
the resulting graph all the connected components are colorful (i.e., any
two vertices of the same color belong to different connected components). We
want to optimize an objective function, the selection of this function
being specific to each problem in the framework.
We analyze three objective functions, and thus, three different problems,
which are believed to be relevant for the biological applications: minimizing
the number of singleton vertices, maximizing the number of edges in the
transitive closure, and minimizing the number of connected components.
Our main result is a polynomial time algorithm for the first problem. This
result disproves the conjecture of Zheng et al. that the problem is -hard
(assuming ). Then, we show that the second problem is -hard,
thus proving and strengthening the conjecture of Zheng et al. that the problem
is -hard. Finally, we show that the third problem does not admit
polynomial time approximation within a factor of for
any , assuming (or within a factor of , assuming ).Comment: 18 pages, 3 figure
On Parameterized Complexity of Group Activity Selection Problems on Social Networks
In Group Activity Selection Problem (GASP), players form coalitions to
participate in activities and have preferences over pairs of the form
(activity, group size). Recently, Igarashi et al. have initiated the study of
group activity selection problems on social networks (gGASP): a group of
players can engage in the same activity if the members of the group form a
connected subset of the underlying communication structure. Igarashi et al.
have primarily focused on Nash stable outcomes, and showed that many associated
algorithmic questions are computationally hard even for very simple networks.
In this paper we study the parameterized complexity of gGASP with respect to
the number of activities as well as with respect to the number of players, for
several solution concepts such as Nash stability, individual stability and core
stability. The first parameter we consider in the number of activities. For
this parameter, we propose an FPT algorithm for Nash stability for the case
where the social network is acyclic and obtain a W[1]-hardness result for
cliques (i.e., for classic GASP); similar results hold for individual
stability. In contrast, finding a core stable outcome is hard even if the
number of activities is bounded by a small constant, both for classic GASP and
when the social network is a star. Another parameter we study is the number of
players. While all solution concepts we consider become polynomial-time
computable when this parameter is bounded by a constant, we prove W[1]-hardness
results for cliques (i.e., for classic GASP).Comment: 9 pages, long version of accepted AAMAS-17 pape
Some results on more flexible versions of Graph Motif
The problems studied in this paper originate from Graph Motif, a problem
introduced in 2006 in the context of biological networks. Informally speaking,
it consists in deciding if a multiset of colors occurs in a connected subgraph
of a vertex-colored graph. Due to the high rate of noise in the biological
data, more flexible definitions of the problem have been outlined. We present
in this paper two inapproximability results for two different optimization
variants of Graph Motif: one where the size of the solution is maximized, the
other when the number of substitutions of colors to obtain the motif from the
solution is minimized. We also study a decision version of Graph Motif where
the connectivity constraint is replaced by the well known notion of graph
modularity. While the problem remains NP-complete, it allows algorithms in FPT
for biologically relevant parameterizations
Parameterized Algorithms for Modular-Width
It is known that a number of natural graph problems which are FPT
parameterized by treewidth become W-hard when parameterized by clique-width. It
is therefore desirable to find a different structural graph parameter which is
as general as possible, covers dense graphs but does not incur such a heavy
algorithmic penalty.
The main contribution of this paper is to consider a parameter called
modular-width, defined using the well-known notion of modular decompositions.
Using a combination of ILPs and dynamic programming we manage to design FPT
algorithms for Coloring and Partitioning into paths (and hence Hamiltonian path
and Hamiltonian cycle), which are W-hard for both clique-width and its recently
introduced restriction, shrub-depth. We thus argue that modular-width occupies
a sweet spot as a graph parameter, generalizing several simpler notions on
dense graphs but still evading the "price of generality" paid by clique-width.Comment: to appear in IPEC 2013. arXiv admin note: text overlap with
arXiv:1304.5479 by other author
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