4,053 research outputs found
Linear orderings of random geometric graphs (extended abstract)
In random geometric graphs, vertices are randomly distributed on [0,1]^2 and pairs of vertices are connected by edges
whenever they are sufficiently close together. Layout problems seek a linear ordering of the vertices of a graph such that a
certain measure is minimized. In this paper, we study several layout problems on random geometric graphs: Bandwidth,
Minimum Linear Arrangement, Minimum Cut, Minimum Sum Cut, Vertex Separation and Bisection. We first prove that
some of these problems remain \NP-complete even for geometric graphs. Afterwards, we compute lower bounds that hold
with high probability on random geometric graphs. Finally, we characterize the probabilistic behavior of the lexicographic
ordering for our layout problems on the class of random geometric graphs.Postprint (published version
Catching the head, tail, and everything in between: a streaming algorithm for the degree distribution
The degree distribution is one of the most fundamental graph properties of
interest for real-world graphs. It has been widely observed in numerous domains
that graphs typically have a tailed or scale-free degree distribution. While
the average degree is usually quite small, the variance is quite high and there
are vertices with degrees at all scales. We focus on the problem of
approximating the degree distribution of a large streaming graph, with small
storage. We design an algorithm headtail, whose main novelty is a new estimator
of infrequent degrees using truncated geometric random variables. We give a
mathematical analysis of headtail and show that it has excellent behavior in
practice. We can process streams will millions of edges with storage less than
1% and get extremely accurate approximations for all scales in the degree
distribution.
We also introduce a new notion of Relative Hausdorff distance between tailed
histograms. Existing notions of distances between distributions are not
suitable, since they ignore infrequent degrees in the tail. The Relative
Hausdorff distance measures deviations at all scales, and is a more suitable
distance for comparing degree distributions. By tracking this new measure, we
are able to give strong empirical evidence of the convergence of headtail
GASP : Geometric Association with Surface Patches
A fundamental challenge to sensory processing tasks in perception and
robotics is the problem of obtaining data associations across views. We present
a robust solution for ascertaining potentially dense surface patch (superpixel)
associations, requiring just range information. Our approach involves
decomposition of a view into regularized surface patches. We represent them as
sequences expressing geometry invariantly over their superpixel neighborhoods,
as uniquely consistent partial orderings. We match these representations
through an optimal sequence comparison metric based on the Damerau-Levenshtein
distance - enabling robust association with quadratic complexity (in contrast
to hitherto employed joint matching formulations which are NP-complete). The
approach is able to perform under wide baselines, heavy rotations, partial
overlaps, significant occlusions and sensor noise.
The technique does not require any priors -- motion or otherwise, and does
not make restrictive assumptions on scene structure and sensor movement. It
does not require appearance -- is hence more widely applicable than appearance
reliant methods, and invulnerable to related ambiguities such as textureless or
aliased content. We present promising qualitative and quantitative results
under diverse settings, along with comparatives with popular approaches based
on range as well as RGB-D data.Comment: International Conference on 3D Vision, 201
Automorphism Groups of Geometrically Represented Graphs
We describe a technique to determine the automorphism group of a
geometrically represented graph, by understanding the structure of the induced
action on all geometric representations. Using this, we characterize
automorphism groups of interval, permutation and circle graphs. We combine
techniques from group theory (products, homomorphisms, actions) with data
structures from computer science (PQ-trees, split trees, modular trees) that
encode all geometric representations.
We prove that interval graphs have the same automorphism groups as trees, and
for a given interval graph, we construct a tree with the same automorphism
group which answers a question of Hanlon [Trans. Amer. Math. Soc 272(2), 1982].
For permutation and circle graphs, we give an inductive characterization by
semidirect and wreath products. We also prove that every abstract group can be
realized by the automorphism group of a comparability graph/poset of the
dimension at most four
Ramsey numbers of ordered graphs
An ordered graph is a pair where is a graph and
is a total ordering of its vertices. The ordered Ramsey number
is the minimum number such that every ordered
complete graph with vertices and with edges colored by two colors contains
a monochromatic copy of .
In contrast with the case of unordered graphs, we show that there are
arbitrarily large ordered matchings on vertices for which
is superpolynomial in . This implies that
ordered Ramsey numbers of the same graph can grow superpolynomially in the size
of the graph in one ordering and remain linear in another ordering.
We also prove that the ordered Ramsey number is
polynomial in the number of vertices of if the bandwidth of
is constant or if is an ordered graph of constant
degeneracy and constant interval chromatic number. The first result gives a
positive answer to a question of Conlon, Fox, Lee, and Sudakov.
For a few special classes of ordered paths, stars or matchings, we give
asymptotically tight bounds on their ordered Ramsey numbers. For so-called
monotone cycles we compute their ordered Ramsey numbers exactly. This result
implies exact formulas for geometric Ramsey numbers of cycles introduced by
K\'arolyi, Pach, T\'oth, and Valtr.Comment: 29 pages, 13 figures, to appear in Electronic Journal of
Combinatoric
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