6,930 research outputs found
Finding long cycles in graphs
We analyze the problem of discovering long cycles inside a graph. We propose
and test two algorithms for this task. The first one is based on recent
advances in statistical mechanics and relies on a message passing procedure.
The second follows a more standard Monte Carlo Markov Chain strategy. Special
attention is devoted to Hamiltonian cycles of (non-regular) random graphs of
minimal connectivity equal to three
Hamilton cycles, minimum degree and bipartite holes
We present a tight extremal threshold for the existence of Hamilton cycles in
graphs with large minimum degree and without a large ``bipartite hole`` (two
disjoint sets of vertices with no edges between them). This result extends
Dirac's classical theorem, and is related to a theorem of Chv\'atal and
Erd\H{o}s.
In detail, an -bipartite-hole in a graph consists of two disjoint
sets of vertices and with and such that there are no
edges between and ; and is the maximum integer
such that contains an -bipartite-hole for every pair of
non-negative integers and with . Our central theorem is that
a graph with at least vertices is Hamiltonian if its minimum degree is
at least .
From the proof we obtain a polynomial time algorithm that either finds a
Hamilton cycle or a large bipartite hole. The theorem also yields a condition
for the existence of edge-disjoint Hamilton cycles. We see that for dense
random graphs , the probability of failing to contain many
edge-disjoint Hamilton cycles is . Finally, we discuss
the complexity of calculating and approximating
Hamilton cycles in graphs and hypergraphs: an extremal perspective
As one of the most fundamental and well-known NP-complete problems, the
Hamilton cycle problem has been the subject of intensive research. Recent
developments in the area have highlighted the crucial role played by the
notions of expansion and quasi-randomness. These concepts and other recent
techniques have led to the solution of several long-standing problems in the
area. New aspects have also emerged, such as resilience, robustness and the
study of Hamilton cycles in hypergraphs. We survey these developments and
highlight open problems, with an emphasis on extremal and probabilistic
approaches.Comment: to appear in the Proceedings of the ICM 2014; due to given page
limits, this final version is slightly shorter than the previous arxiv
versio
Gibbs and Quantum Discrete Spaces
Gibbs measure is one of the central objects of the modern probability,
mathematical statistical physics and euclidean quantum field theory. Here we
define and study its natural generalization for the case when the space, where
the random field is defined is itself random. Moreover, this randomness is not
given apriori and independently of the configuration, but rather they depend on
each other, and both are given by Gibbs procedure; We call the resulting object
a Gibbs family because it parametrizes Gibbs fields on different graphs in the
support of the distribution. We study also quantum (KMS) analog of Gibbs
families. Various applications to discrete quantum gravity are given.Comment: 37 pages, 2 figure
Threshold phenomena in random graphs
In the 1950s, random graphs appeared for the first time in a result of the prolific hungarian mathematician Pál Erd\H{o}s. Since then, interest in random graph theory has only grown up until now. In its first stages, the basis of its theory were set, while they were mainly used in probability and combinatorics theory. However, with the new century and the boom of technologies like the World Wide Web, random graphs are even more important since they are extremely useful to handle problems in fields like network and communication theory. Because of this fact, nowadays random graphs are widely studied by the mathematical community around the world and new promising results have been recently achieved, showing an exciting future for this field. In this bachelor thesis, we focus our study on the threshold phenomena for graph properties within random graphs
An algorithm for counting circuits: application to real-world and random graphs
We introduce an algorithm which estimates the number of circuits in a graph
as a function of their length. This approach provides analytical results for
the typical entropy of circuits in sparse random graphs. When applied to
real-world networks, it allows to estimate exponentially large numbers of
circuits in polynomial time. We illustrate the method by studying a graph of
the Internet structure.Comment: 7 pages, 3 figures, minor corrections, accepted versio
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