28,790 research outputs found
Modeling heterogeneity in random graphs through latent space models: a selective review
We present a selective review on probabilistic modeling of heterogeneity in
random graphs. We focus on latent space models and more particularly on
stochastic block models and their extensions that have undergone major
developments in the last five years
Uniform regular weighted graphs with large degree: Wigner's law, asymptotic freeness and graphons limit
For each , let be a simple random graph on the set of vertices
, which is invariant by relabeling of the vertices. The
asymptotic behavior as goes to infinity of correlation functions:
furnishes informations on the asymptotic
spectral properties of the adjacency matrix of . Denote by and assume . If for any
, the standardized empirical eigenvalue distribution of converges in
expectation to the semicircular law and the matrix satisfies asymptotic
freeness properties in the sense of free probability theory. We provide such
estimates for uniform -regular graphs , under the additional
assumption that for some . Our method applies also for
simple graphs whose edges are labelled by i.i.d. random variables.Comment: 21 pages, 7 figure
Limit theory for geometric statistics of point processes having fast decay of correlations
Let be a simple,stationary point process having fast decay of
correlations, i.e., its correlation functions factorize up to an additive error
decaying faster than any power of the separation distance. Let be its restriction to windows . We consider the statistic where denotes a score function
representing the interaction of with respect to . When depends
on local data in the sense that its radius of stabilization has an exponential
tail, we establish expectation asymptotics, variance asymptotics, and CLT for
and, more generally, for statistics of the re-scaled, possibly
signed, -weighted point measures , as . This gives the
limit theory for non-linear geometric statistics (such as clique counts,
intrinsic volumes of the Boolean model, and total edge length of the
-nearest neighbors graph) of -determinantal point processes having
fast decreasing kernels extending the CLTs of Soshnikov (2002) to non-linear
statistics. It also gives the limit theory for geometric U-statistics of
-permanental point processes and the zero set of Gaussian entire
functions, extending the CLTs of Nazarov and Sodin (2012) and Shirai and
Takahashi (2003), which are also confined to linear statistics. The proof of
the central limit theorem relies on a factorial moment expansion originating in
Blaszczyszyn (1995), Blaszczyszyn, Merzbach, Schmidt (1997) to show the fast
decay of the correlations of -weighted point measures. The latter property
is shown to imply a condition equivalent to Brillinger mixing and consequently
yields the CLT for via an extension of the cumulant method.Comment: 62 pages. Fundamental changes to the terminology including the title.
The earlier 'clustering' condition is now introduced as a notion of mixing
and its connection to Brillinger mixing is remarked. Newer results for
superposition of independent point processes have been adde
Inapproximability for Antiferromagnetic Spin Systems in the Tree Non-Uniqueness Region
A remarkable connection has been established for antiferromagnetic 2-spin
systems, including the Ising and hard-core models, showing that the
computational complexity of approximating the partition function for graphs
with maximum degree D undergoes a phase transition that coincides with the
statistical physics uniqueness/non-uniqueness phase transition on the infinite
D-regular tree. Despite this clear picture for 2-spin systems, there is little
known for multi-spin systems. We present the first analog of the above
inapproximability results for multi-spin systems.
The main difficulty in previous inapproximability results was analyzing the
behavior of the model on random D-regular bipartite graphs, which served as the
gadget in the reduction. To this end one needs to understand the moments of the
partition function. Our key contribution is connecting: (i) induced matrix
norms, (ii) maxima of the expectation of the partition function, and (iii)
attractive fixed points of the associated tree recursions (belief propagation).
The view through matrix norms allows a simple and generic analysis of the
second moment for any spin system on random D-regular bipartite graphs. This
yields concentration results for any spin system in which one can analyze the
maxima of the first moment. The connection to fixed points of the tree
recursions enables an analysis of the maxima of the first moment for specific
models of interest.
For k-colorings we prove that for even k, in the tree non-uniqueness region
(which corresponds to k<D) it is NP-hard, unless NP=RP, to approximate the
number of colorings for triangle-free D-regular graphs. Our proof extends to
the antiferromagnetic Potts model, and, in fact, to every antiferromagnetic
model under a mild condition
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