83,251 research outputs found
Pixel and Voxel Representations of Graphs
We study contact representations for graphs, which we call pixel
representations in 2D and voxel representations in 3D. Our representations are
based on the unit square grid whose cells we call pixels in 2D and voxels in
3D. Two pixels are adjacent if they share an edge, two voxels if they share a
face. We call a connected set of pixels or voxels a blob. Given a graph, we
represent its vertices by disjoint blobs such that two blobs contain adjacent
pixels or voxels if and only if the corresponding vertices are adjacent. We are
interested in the size of a representation, which is the number of pixels or
voxels it consists of.
We first show that finding minimum-size representations is NP-complete. Then,
we bound representation sizes needed for certain graph classes. In 2D, we show
that, for -outerplanar graphs with vertices, pixels are
always sufficient and sometimes necessary. In particular, outerplanar graphs
can be represented with a linear number of pixels, whereas general planar
graphs sometimes need a quadratic number. In 3D, voxels are
always sufficient and sometimes necessary for any -vertex graph. We improve
this bound to for graphs of treewidth and to
for graphs of genus . In particular, planar graphs
admit representations with voxels
Recognizing Planar Laman Graphs
Laman graphs are the minimally rigid graphs in the plane. We present two algorithms for recognizing planar Laman graphs. A simple algorithm with running time O(n^(3/2)) and a more complicated algorithm with running time O(n log^3 n) based on involved planar network flow algorithms. Both improve upon the previously fastest algorithm for general graphs by Gabow and Westermann [Algorithmica, 7(5-6):465 - 497, 1992] with running time O(n sqrt{n log n}).
To solve this problem we introduce two algorithms (with the running times stated above) that check whether for a directed planar graph G, disjoint sets S, T subseteq V(G), and a fixed k the following connectivity condition holds: for each vertex s in S there are k directed paths from s to T pairwise having only vertex s in common. This variant of connectivity seems interesting on its own
Time-Space Trade-Offs for Computing Euclidean Minimum Spanning Trees
In the limited-workspace model, we assume that the input of size lies in
a random access read-only memory. The output has to be reported sequentially,
and it cannot be accessed or modified. In addition, there is a read-write
workspace of words, where is a given parameter.
In a time-space trade-off, we are interested in how the running time of an
algorithm improves as varies from to .
We present a time-space trade-off for computing the Euclidean minimum
spanning tree (EMST) of a set of sites in the plane. We present an
algorithm that computes EMST using time and
words of workspace. Our algorithm uses the fact that EMST is a subgraph of
the bounded-degree relative neighborhood graph of , and applies Kruskal's
MST algorithm on it. To achieve this with limited workspace, we introduce a
compact representation of planar graphs, called an -net which allows us to
manipulate its component structure during the execution of the algorithm
Minimum Cycle Basis and All-Pairs Min Cut of a Planar Graph in Subquadratic Time
A minimum cycle basis of a weighted undirected graph is a basis of the
cycle space of such that the total weight of the cycles in this basis is
minimized. If is a planar graph with non-negative edge weights, such a
basis can be found in time and space, where is the size of . We
show that this is optimal if an explicit representation of the basis is
required. We then present an time and space
algorithm that computes a minimum cycle basis \emph{implicitly}. From this
result, we obtain an output-sensitive algorithm that explicitly computes a
minimum cycle basis in time and space,
where is the total size (number of edges and vertices) of the cycles in the
basis. These bounds reduce to and ,
respectively, when is unweighted. We get similar results for the all-pairs
min cut problem since it is dual equivalent to the minimum cycle basis problem
for planar graphs. We also obtain time and
space algorithms for finding, respectively, the weight vector and a Gomory-Hu
tree of . The previous best time and space bound for these two problems was
quadratic. From our Gomory-Hu tree algorithm, we obtain the following result:
with time and space for preprocessing, the
weight of a min cut between any two given vertices of can be reported in
constant time. Previously, such an oracle required quadratic time and space for
preprocessing. The oracle can also be extended to report the actual cut in time
proportional to its size
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