90,046 research outputs found
Uniform random sampling of planar graphs in linear time
This article introduces new algorithms for the uniform random generation of
labelled planar graphs. Its principles rely on Boltzmann samplers, as recently
developed by Duchon, Flajolet, Louchard, and Schaeffer. It combines the
Boltzmann framework, a suitable use of rejection, a new combinatorial bijection
found by Fusy, Poulalhon and Schaeffer, as well as a precise analytic
description of the generating functions counting planar graphs, which was
recently obtained by Gim\'enez and Noy. This gives rise to an extremely
efficient algorithm for the random generation of planar graphs. There is a
preprocessing step of some fixed small cost. Then, the expected time complexity
of generation is quadratic for exact-size uniform sampling and linear for
approximate-size sampling. This greatly improves on the best previously known
time complexity for exact-size uniform sampling of planar graphs with
vertices, which was a little over .Comment: 55 page
Generation, Ranking and Unranking of Ordered Trees with Degree Bounds
We study the problem of generating, ranking and unranking of unlabeled
ordered trees whose nodes have maximum degree of . This class of trees
represents a generalization of chemical trees. A chemical tree is an unlabeled
tree in which no node has degree greater than 4. By allowing up to
children for each node of chemical tree instead of 4, we will have a
generalization of chemical trees. Here, we introduce a new encoding over an
alphabet of size 4 for representing unlabeled ordered trees with maximum degree
of . We use this encoding for generating these trees in A-order with
constant average time and O(n) worst case time. Due to the given encoding, with
a precomputation of size and time O(n^2) (assuming is constant), both
ranking and unranking algorithms are also designed taking O(n) and O(nlogn)
time complexities.Comment: In Proceedings DCM 2015, arXiv:1603.0053
A constant-time algorithm for middle levels Gray codes
For any integer a middle levels Gray code is a cyclic listing of
all -element and -element subsets of such that
any two consecutive subsets differ in adding or removing a single element. The
question whether such a Gray code exists for any has been the subject
of intensive research during the last 30 years, and has been answered
affirmatively only recently [T. M\"utze. Proof of the middle levels conjecture.
Proc. London Math. Soc., 112(4):677--713, 2016]. In a follow-up paper [T.
M\"utze and J. Nummenpalo. An efficient algorithm for computing a middle levels
Gray code. To appear in ACM Transactions on Algorithms, 2018] this existence
proof was turned into an algorithm that computes each new set in the Gray code
in time on average. In this work we present an algorithm for
computing a middle levels Gray code in optimal time and space: each new set is
generated in time on average, and the required space is
Random-bit optimal uniform sampling for rooted planar trees with given sequence of degrees and Applications
In this paper, we redesign and simplify an algorithm due to Remy et al. for
the generation of rooted planar trees that satisfies a given partition of
degrees. This new version is now optimal in terms of random bit complexity, up
to a multiplicative constant. We then apply a natural process
"simulate-guess-and-proof" to analyze the height of a random Motzkin in
function of its frequency of unary nodes. When the number of unary nodes
dominates, we prove some unconventional height phenomenon (i.e. outside the
universal square root behaviour.)Comment: 19 page
Persistence of the Jordan center in Random Growing Trees
The Jordan center of a graph is defined as a vertex whose maximum distance to
other nodes in the graph is minimal, and it finds applications in facility
location and source detection problems. We study properties of the Jordan
Center in the case of random growing trees. In particular, we consider a
regular tree graph on which an infection starts from a root node and then
spreads along the edges of the graph according to various random spread models.
For the Independent Cascade (IC) model and the discrete Susceptible Infected
(SI) model, both of which are discrete time models, we show that as the
infected subgraph grows with time, the Jordan center persists on a single
vertex after a finite number of timesteps. Finally, we also study the
continuous time version of the SI model and bound the maximum distance between
the Jordan center and the root node at any time.Comment: 28 pages, 14 figure
Entropy and Hausdorff Dimension in Random Growing Trees
We investigate the limiting behavior of random tree growth in preferential
attachment models. The tree stems from a root, and we add vertices to the
system one-by-one at random, according to a rule which depends on the degree
distribution of the already existing tree. The so-called weight function, in
terms of which the rule of attachment is formulated, is such that each vertex
in the tree can have at most K children. We define the concept of a certain
random measure mu on the leaves of the limiting tree, which captures a global
property of the tree growth in a natural way. We prove that the Hausdorff and
the packing dimension of this limiting measure is equal and constant with
probability one. Moreover, the local dimension of mu equals the Hausdorff
dimension at mu-almost every point. We give an explicit formula for the
dimension, given the rule of attachment
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