156 research outputs found
Methods and problems of wavelength-routing in all-optical networks
We give a survey of recent theoretical results obtained for wavelength-routing in all-optical networks. The survey is based on the previous survey in [Beauquier, B., Bermond, J-C., Gargano, L., Hell, P., Perennes, S., Vaccaro, U.: Graph problems arising from wavelength-routing in all-optical networks. In: Proc. of the 2nd Workshop on Optics and Computer Science, part of IPPS'97, 1997]. We focus our survey on the current research directions and on the used methods. We also state several open problems connected with this line of research, and give an overview of several related research directions
Parameterized and approximation complexity of the detection pair problem in graphs
We study the complexity of the problem DETECTION PAIR. A detection pair of a
graph is a pair of sets of detectors with , the
watchers, and , the listeners, such that for every pair
of vertices that are not dominated by a watcher of , there is a listener of
whose distances to and to are different. The goal is to minimize
. This problem generalizes the two classic problems DOMINATING SET and
METRIC DIMENSION, that correspond to the restrictions and
, respectively. DETECTION PAIR was recently introduced by Finbow,
Hartnell and Young [A. S. Finbow, B. L. Hartnell and J. R. Young. The
complexity of monitoring a network with both watchers and listeners.
Manuscript, 2015], who proved it to be NP-complete on trees, a surprising
result given that both DOMINATING SET and METRIC DIMENSION are known to be
linear-time solvable on trees. It follows from an existing reduction by Hartung
and Nichterlein for METRIC DIMENSION that even on bipartite subcubic graphs of
arbitrarily large girth, DETECTION PAIR is NP-hard to approximate within a
sub-logarithmic factor and W[2]-hard (when parameterized by solution size). We
show, using a reduction to SET COVER, that DETECTION PAIR is approximable
within a factor logarithmic in the number of vertices of the input graph. Our
two main results are a linear-time -approximation algorithm and an FPT
algorithm for DETECTION PAIR on trees.Comment: 13 page
Centroidal bases in graphs
We introduce the notion of a centroidal locating set of a graph , that is,
a set of vertices such that all vertices in are uniquely determined by
their relative distances to the vertices of . A centroidal locating set of
of minimum size is called a centroidal basis, and its size is the
centroidal dimension . This notion, which is related to previous
concepts, gives a new way of identifying the vertices of a graph. The
centroidal dimension of a graph is lower- and upper-bounded by the metric
dimension and twice the location-domination number of , respectively. The
latter two parameters are standard and well-studied notions in the field of
graph identification.
We show that for any graph with vertices and maximum degree at
least~2, . We discuss the
tightness of these bounds and in particular, we characterize the set of graphs
reaching the upper bound. We then show that for graphs in which every pair of
vertices is connected via a bounded number of paths,
, the bound being tight for paths and
cycles. We finally investigate the computational complexity of determining
for an input graph , showing that the problem is hard and cannot
even be approximated efficiently up to a factor of . We also give an
-approximation algorithm
Improved Analysis of Deterministic Load-Balancing Schemes
We consider the problem of deterministic load balancing of tokens in the
discrete model. A set of processors is connected into a -regular
undirected network. In every time step, each processor exchanges some of its
tokens with each of its neighbors in the network. The goal is to minimize the
discrepancy between the number of tokens on the most-loaded and the
least-loaded processor as quickly as possible.
Rabani et al. (1998) present a general technique for the analysis of a wide
class of discrete load balancing algorithms. Their approach is to characterize
the deviation between the actual loads of a discrete balancing algorithm with
the distribution generated by a related Markov chain. The Markov chain can also
be regarded as the underlying model of a continuous diffusion algorithm. Rabani
et al. showed that after time , any algorithm of their
class achieves a discrepancy of , where is the spectral
gap of the transition matrix of the graph, and is the initial load
discrepancy in the system.
In this work we identify some natural additional conditions on deterministic
balancing algorithms, resulting in a class of algorithms reaching a smaller
discrepancy. This class contains well-known algorithms, eg., the Rotor-Router.
Specifically, we introduce the notion of cumulatively fair load-balancing
algorithms where in any interval of consecutive time steps, the total number of
tokens sent out over an edge by a node is the same (up to constants) for all
adjacent edges. We prove that algorithms which are cumulatively fair and where
every node retains a sufficient part of its load in each step, achieve a
discrepancy of in time . We
also show that in general neither of these assumptions may be omitted without
increasing discrepancy. We then show by a combinatorial potential reduction
argument that any cumulatively fair scheme satisfying some additional
assumptions achieves a discrepancy of almost as quickly as the
continuous diffusion process. This positive result applies to some of the
simplest and most natural discrete load balancing schemes.Comment: minor corrections; updated literature overvie
From Balls and Bins to Points and Vertices
Given a graph G = (V, E) with |V| = n, we consider the following problem. Place m = n points on the vertices of G independently and uniformly at random. Once the points are placed, relocate them using a bijection from the points to the vertices that minimizes the maximum distance between the random place of the points and their target vertices. We look for an upper bound on this maximum relocation distance that holds with high probability (over the initial placements of the points). For general graphs and in the case m ≤ n, we prove the #P -hardness of the problem and that the maximum relocation distance is O(√n) with high probability. We present a Fully Polynomial Randomized Approximation Scheme when the input graph admits a polynomial-size family of witness cuts while for trees we provide a 2-approximation algorithm. Many applications concern the variation in which m = (1 − ǫ)n for some 0 < ǫ < 1. We provide several bounds for the maximum relocation distance according to different graph topologies
On the distance-edge-monitoring numbers of graphs
Foucaud et al. [Discrete Appl. Math. 319 (2022), 424-438] recently introduced
and initiated the study of a new graph-theoretic concept in the area of network
monitoring. For a set of vertices and an edge of a graph , let be the set of pairs with a vertex of and a vertex of
such that . For a vertex , let
be the set of edges such that there exists a vertex in with . A set of vertices of a graph is
distance-edge-monitoring set if every edge of is monitored by some
vertex of , that is, the set is nonempty. The
distance-edge-monitoring number of a graph , denoted by , is defined
as the smallest size of distance-edge-monitoring sets of . The vertices of
represent distance probes in a network modeled by ; when the edge
fails, the distance from to increases, and thus we are able to detect
the failure. It turns out that not only we can detect it, but we can even
correctly locate the failing edge. In this paper, we continue the study of
\emph{distance-edge-monitoring sets}. In particular, we give upper and lower
bounds of , , , respectively, and extremal graphs
attaining the bounds are characterized. We also characterize the graphs with
Constructing Incremental Sequences in Graphs
Given a weighted graph , we investigate the problem of constructing a sequence of subsets of vertices (called groups) with small diameters, where the diameter of a group is calculated using distances in G. The constraint on these n groups is that they must be incremental: . The cost of a sequence is the maximum ratio between the diameter of each group Mi and the diameter of a group with I vertices and minimum diameter:
.
This quantity captures the impact of the incremental constraint on the diameters of the groups in a sequence. We give general bounds on the value of this ratio and we prove that the problem of constructing an optimal incremental sequence cannot be solved approximately in polynomial time with an approximation ratio less than 2 unless P = NP. Finally, we give a 4-approximation algorithm and we show that the analysis of our algorithm is tight
Monitoring the edges of product networks using distances
Foucaud {\it et al.} recently introduced and initiated the study of a new
graph-theoretic concept in the area of network monitoring. Let be a graph
with vertex set , a subset of , and be an edge in ,
and let be the set of pairs such that where and . is called a
\emph{distance-edge-monitoring set} if every edge of is monitored by
some vertex of , that is, the set is nonempty. The {\em
distance-edge-monitoring number} of , denoted by , is
defined as the smallest size of distance-edge-monitoring sets of . For two
graphs of order , respectively, in this paper we prove that
, where is the Cartesian
product operation. Moreover, we characterize the graphs attaining the upper and
lower bounds and show their applications on some known networks. We also obtain
the distance-edge-monitoring numbers of join, corona, cluster, and some
specific networks.Comment: 19 page
Constructing disjoint Steiner trees in Sierpi\'{n}ski graphs
Let be a graph and with . Then the trees
in are \emph{internally disjoint Steiner trees}
connecting (or -Steiner trees) if and
for every pair of distinct integers , . Similarly, if we only have the condition but without the condition , then they are
\emph{edge-disjoint Steiner trees}. The \emph{generalized -connectivity},
denoted by , of a graph , is defined as
,
where is the maximum number of internally disjoint -Steiner
trees. The \emph{generalized local edge-connectivity} is the
maximum number of edge-disjoint Steiner trees connecting in . The {\it
generalized -edge-connectivity} of is defined as
. These
measures are generalizations of the concepts of connectivity and
edge-connectivity, and they and can be used as measures of vulnerability of
networks. It is, in general, difficult to compute these generalized
connectivities. However, there are precise results for some special classes of
graphs. In this paper, we obtain the exact value of
for , and the exact value of for
, where is the Sierpi\'{n}ski graphs with order
. As a direct consequence, these graphs provide additional interesting
examples when . We also study the
some network properties of Sierpi\'{n}ski graphs
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