4,193 research outputs found
Parallel Graph Decompositions Using Random Shifts
We show an improved parallel algorithm for decomposing an undirected
unweighted graph into small diameter pieces with a small fraction of the edges
in between. These decompositions form critical subroutines in a number of graph
algorithms. Our algorithm builds upon the shifted shortest path approach
introduced in [Blelloch, Gupta, Koutis, Miller, Peng, Tangwongsan, SPAA 2011].
By combining various stages of the previous algorithm, we obtain a
significantly simpler algorithm with the same asymptotic guarantees as the best
sequential algorithm
Distributed Strong Diameter Network Decomposition
For a pair of positive parameters , a partition of the
vertex set of an -vertex graph into disjoint clusters of
diameter at most each is called a network decomposition, if the
supergraph , obtained by contracting each of the clusters
of , can be properly -colored. The decomposition is
said to be strong (resp., weak) if each of the clusters has strong (resp.,
weak) diameter at most , i.e., if for every cluster and
every two vertices , the distance between them in the induced graph
of (resp., in ) is at most .
Network decomposition is a powerful construct, very useful in distributed
computing and beyond. It was shown by Awerbuch \etal \cite{AGLP89} and
Panconesi and Srinivasan \cite{PS92}, that strong network decompositions can be computed in
distributed time. Linial and Saks \cite{LS93} devised an
ingenious randomized algorithm that constructs {\em weak} network decompositions in time. It was however open till now
if {\em strong} network decompositions with both parameters can be constructed in distributed time.
In this paper we answer this long-standing open question in the affirmative,
and show that strong network decompositions can be
computed in time. We also present a tradeoff between parameters
of our network decomposition. Our work is inspired by and relies on the
"shifted shortest path approach", due to Blelloch \etal \cite{BGKMPT11}, and
Miller \etal \cite{MPX13}. These authors developed this approach for PRAM
algorithms for padded partitions. We adapt their approach to network
decompositions in the distributed model of computation
Low Diameter Graph Decompositions by Approximate Distance Computation
In many models for large-scale computation, decomposition of the problem is key to efficient algorithms. For distance-related graph problems, it is often crucial that such a decomposition results in clusters of small diameter, while the probability that an edge is cut by the decomposition scales linearly with the length of the edge. There is a large body of literature on low diameter graph decomposition with small edge cutting probabilities, with all existing techniques heavily building on single source shortest paths (SSSP) computations. Unfortunately, in many theoretical models for large-scale computations, the SSSP task constitutes a complexity bottleneck. Therefore, it is desirable to replace exact SSSP computations with approximate ones. However this imposes a fundamental challenge since the existing constructions of low diameter graph decomposition with small edge cutting probabilities inherently rely on the subtractive form of the triangle inequality, which fails to hold under distance approximation.
The current paper overcomes this obstacle by developing a technique termed blurry ball growing. By combining this technique with a clever algorithmic idea of Miller et al. (SPAA 2013), we obtain a construction of low diameter decompositions with small edge cutting probabilities which replaces exact SSSP computations by (a small number of) approximate ones. The utility of our approach is showcased by deriving efficient algorithms that work in the CONGEST, PRAM, and semi-streaming models of computation. As an application, we obtain metric tree embedding algorithms in the vein of Bartal (FOCS 1996) whose computational complexities in these models are optimal up to polylogarithmic factors. Our embeddings have the additional useful property that the tree can be mapped back to the original graph such that each edge is "used" only logaritmically many times, which is of interest for capacitated problems and simulating CONGEST algorithms on the tree into which the graph is embedded
Space and Time Efficient Parallel Graph Decomposition, Clustering, and Diameter Approximation
We develop a novel parallel decomposition strategy for unweighted, undirected
graphs, based on growing disjoint connected clusters from batches of centers
progressively selected from yet uncovered nodes. With respect to similar
previous decompositions, our strategy exercises a tighter control on both the
number of clusters and their maximum radius.
We present two important applications of our parallel graph decomposition:
(1) -center clustering approximation; and (2) diameter approximation. In
both cases, we obtain algorithms which feature a polylogarithmic approximation
factor and are amenable to a distributed implementation that is geared for
massive (long-diameter) graphs. The total space needed for the computation is
linear in the problem size, and the parallel depth is substantially sublinear
in the diameter for graphs with low doubling dimension. To the best of our
knowledge, ours are the first parallel approximations for these problems which
achieve sub-diameter parallel time, for a relevant class of graphs, using only
linear space. Besides the theoretical guarantees, our algorithms allow for a
very simple implementation on clustered architectures: we report on extensive
experiments which demonstrate their effectiveness and efficiency on large
graphs as compared to alternative known approaches.Comment: 14 page
Faster and Simpler Distributed Algorithms for Testing and Correcting Graph Properties in the CONGEST-Model
In this paper we present distributed testing algorithms of graph properties
in the CONGEST-model [Censor-Hillel et al. 2016]. We present one-sided error
testing algorithms in the general graph model.
We first describe a general procedure for converting -testers with
a number of rounds , where denotes the diameter of the graph, to
rounds, where is the number of
processors of the network. We then apply this procedure to obtain an optimal
tester, in terms of , for testing bipartiteness, whose round complexity is
, which improves over the -round algorithm by Censor-Hillel et al. (DISC 2016). Moreover, for
cycle-freeness, we obtain a \emph{corrector} of the graph that locally corrects
the graph so that the corrected graph is acyclic. Note that, unlike a tester, a
corrector needs to mend the graph in many places in the case that the graph is
far from having the property.
In the second part of the paper we design algorithms for testing whether the
network is -free for any connected of size up to four with round
complexity of . This improves over the
-round algorithms for testing triangle freeness by
Censor-Hillel et al. (DISC 2016) and for testing excluded graphs of size by
Fraigniaud et al. (DISC 2016).
In the last part we generalize the global tester by Iwama and Yoshida (ITCS
2014) of testing -path freeness to testing the exclusion of any tree of
order . We then show how to simulate this algorithm in the CONGEST-model in
rounds
Schnyder decompositions for regular plane graphs and application to drawing
Schnyder woods are decompositions of simple triangulations into three
edge-disjoint spanning trees crossing each other in a specific way. In this
article, we define a generalization of Schnyder woods to -angulations (plane
graphs with faces of degree ) for all . A \emph{Schnyder
decomposition} is a set of spanning forests crossing each other in a
specific way, and such that each internal edge is part of exactly of the
spanning forests. We show that a Schnyder decomposition exists if and only if
the girth of the -angulation is . As in the case of Schnyder woods
(), there are alternative formulations in terms of orientations
("fractional" orientations when ) and in terms of corner-labellings.
Moreover, the set of Schnyder decompositions on a fixed -angulation of girth
is a distributive lattice. We also show that the structures dual to
Schnyder decompositions (on -regular plane graphs of mincut rooted at a
vertex ) are decompositions into spanning trees rooted at such
that each edge not incident to is used in opposite directions by two
trees. Additionally, for even values of , we show that a subclass of
Schnyder decompositions, which are called even, enjoy additional properties
that yield a reduced formulation; in the case d=4, these correspond to
well-studied structures on simple quadrangulations (2-orientations and
partitions into 2 spanning trees). In the case d=4, the dual of even Schnyder
decompositions yields (planar) orthogonal and straight-line drawing algorithms.
For a 4-regular plane graph of mincut 4 with vertices plus a marked
vertex , the vertices of are placed on a grid according to a permutation pattern, and in the orthogonal drawing
each of the edges of has exactly one bend. Embedding
also the marked vertex is doable at the cost of two additional rows and
columns and 8 additional bends for the 4 edges incident to . We propose a
further compaction step for the drawing algorithm and show that the obtained
grid-size is strongly concentrated around for a uniformly
random instance with vertices
Improved Parallel Algorithms for Spanners and Hopsets
We use exponential start time clustering to design faster and more
work-efficient parallel graph algorithms involving distances. Previous
algorithms usually rely on graph decomposition routines with strict
restrictions on the diameters of the decomposed pieces. We weaken these bounds
in favor of stronger local probabilistic guarantees. This allows more direct
analyses of the overall process, giving: * Linear work parallel algorithms that
construct spanners with stretch and size in unweighted
graphs, and size in weighted graphs. * Hopsets that lead
to the first parallel algorithm for approximating shortest paths in undirected
graphs with work
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