699 research outputs found
Generating constrained random graphs using multiple edge switches
The generation of random graphs using edge swaps provides a reliable method
to draw uniformly random samples of sets of graphs respecting some simple
constraints, e.g. degree distributions. However, in general, it is not
necessarily possible to access all graphs obeying some given con- straints
through a classical switching procedure calling on pairs of edges. We therefore
propose to get round this issue by generalizing this classical approach through
the use of higher-order edge switches. This method, which we denote by "k-edge
switching", makes it possible to progres- sively improve the covered portion of
a set of constrained graphs, thereby providing an increasing, asymptotically
certain confidence on the statistical representativeness of the obtained
sample.Comment: 15 page
Estimating parameters of a multipartite loglinear graph model via the EM algorithm
We will amalgamate the Rash model (for rectangular binary tables) and the
newly introduced - models (for random undirected graphs) in the
framework of a semiparametric probabilistic graph model. Our purpose is to give
a partition of the vertices of an observed graph so that the generated
subgraphs and bipartite graphs obey these models, where their strongly
connected parameters give multiscale evaluation of the vertices at the same
time. In this way, a heterogeneous version of the stochastic block model is
built via mixtures of loglinear models and the parameters are estimated with a
special EM iteration. In the context of social networks, the clusters can be
identified with social groups and the parameters with attitudes of people of
one group towards people of the other, which attitudes depend on the cluster
memberships. The algorithm is applied to randomly generated and real-word data
The mixing time of the switch Markov chains: a unified approach
Since 1997 a considerable effort has been spent to study the mixing time of
switch Markov chains on the realizations of graphic degree sequences of simple
graphs. Several results were proved on rapidly mixing Markov chains on
unconstrained, bipartite, and directed sequences, using different mechanisms.
The aim of this paper is to unify these approaches. We will illustrate the
strength of the unified method by showing that on any -stable family of
unconstrained/bipartite/directed degree sequences the switch Markov chain is
rapidly mixing. This is a common generalization of every known result that
shows the rapid mixing nature of the switch Markov chain on a region of degree
sequences. Two applications of this general result will be presented. One is an
almost uniform sampler for power-law degree sequences with exponent
. The other one shows that the switch Markov chain on the
degree sequence of an Erd\H{o}s-R\'enyi random graph is asymptotically
almost surely rapidly mixing if is bounded away from 0 and 1 by at least
.Comment: Clarification
Approximate Sampling and Counting of Graphs with Near-Regular Degree Intervals
The approximate uniform sampling of graphs with a given degree sequence is a well-known, extensively studied problem in theoretical computer science and has significant applications, e.g., in the analysis of social networks. In this work we study an extension of the problem, where degree intervals are specified rather than a single degree sequence. We are interested in sampling and counting graphs whose degree sequences satisfy the degree interval constraints. A natural scenario where this problem arises is in hypothesis testing on social networks that are only partially observed. In this work, we provide the first fully polynomial almost uniform sampler (FPAUS) as well as the first fully polynomial randomized approximation scheme (FPRAS) for sampling and counting, respectively, graphs with near-regular degree intervals, in which every node has a degree from an interval not too far away from a given . In order to design our FPAUS, we rely on various state-of-the-art tools from Markov chain theory and combinatorics. In particular, we provide the first non-trivial algorithmic application of a breakthrough result of Liebenau and Wormald (2017) regarding an asymptotic formula for the number of graphs with a given near-regular degree sequence. Furthermore, we also make use of the recent breakthrough of Anari et al. (2019) on sampling a base of a matroid under a strongly log-concave probability distribution. As a more direct approach, we also study a natural Markov chain recently introduced by Rechner, Strowick and M\"uller-Hannemann (2018), based on three simple local operations: Switches, hinge flips, and additions/deletions of a single edge. We obtain the first theoretical results for this Markov chain by showing it is rapidly mixing for the case of near-regular degree intervals of size at most one
The number of graphs and a random graph with a given degree sequence
We consider the set of all graphs on n labeled vertices with prescribed
degrees D=(d_1, ..., d_n). For a wide class of tame degree sequences D we prove
a computationally efficient asymptotic formula approximating the number of
graphs within a relative error which approaches 0 as n grows. As a corollary,
we prove that the structure of a random graph with a given tame degree sequence
D is well described by a certain maximum entropy matrix computed from D. We
also establish an asymptotic formula for the number of bipartite graphs with
prescribed degrees of vertices, or, equivalently, for the number of 0-1
matrices with prescribed row and column sums.Comment: 52 pages, minor improvement
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