28,844 research outputs found
Computational and Statistical Thresholds in Multi-layer Stochastic Block Models
We study the problem of community recovery and detection in multi-layer
stochastic block models, focusing on the critical network density threshold for
consistent community structure inference. Using a prototypical two-block model,
we reveal a computational barrier for such multi-layer stochastic block models
that does not exist for its single-layer counterpart: When there are no
computational constraints, the density threshold depends linearly on the number
of layers. However, when restricted to polynomial-time algorithms, the density
threshold scales with the square root of the number of layers, assuming
correctness of a low-degree polynomial hardness conjecture. Our results provide
a nearly complete picture of the optimal inference in multiple-layer stochastic
block models and partially settle the open question in Lei and Lin (2022)
regarding the optimality of the bias-adjusted spectral method.Comment: 31 page
Training Input-Output Recurrent Neural Networks through Spectral Methods
We consider the problem of training input-output recurrent neural networks
(RNN) for sequence labeling tasks. We propose a novel spectral approach for
learning the network parameters. It is based on decomposition of the
cross-moment tensor between the output and a non-linear transformation of the
input, based on score functions. We guarantee consistent learning with
polynomial sample and computational complexity under transparent conditions
such as non-degeneracy of model parameters, polynomial activations for the
neurons, and a Markovian evolution of the input sequence. We also extend our
results to Bidirectional RNN which uses both previous and future information to
output the label at each time point, and is employed in many NLP tasks such as
POS tagging
Super-resolution Line Spectrum Estimation with Block Priors
We address the problem of super-resolution line spectrum estimation of an
undersampled signal with block prior information. The component frequencies of
the signal are assumed to take arbitrary continuous values in known frequency
blocks. We formulate a general semidefinite program to recover these
continuous-valued frequencies using theories of positive trigonometric
polynomials. The proposed semidefinite program achieves super-resolution
frequency recovery by taking advantage of known structures of frequency blocks.
Numerical experiments show great performance enhancements using our method.Comment: 7 pages, double colum
Graph learning under sparsity priors
Graph signals offer a very generic and natural representation for data that
lives on networks or irregular structures. The actual data structure is however
often unknown a priori but can sometimes be estimated from the knowledge of the
application domain. If this is not possible, the data structure has to be
inferred from the mere signal observations. This is exactly the problem that we
address in this paper, under the assumption that the graph signals can be
represented as a sparse linear combination of a few atoms of a structured graph
dictionary. The dictionary is constructed on polynomials of the graph
Laplacian, which can sparsely represent a general class of graph signals
composed of localized patterns on the graph. We formulate a graph learning
problem, whose solution provides an ideal fit between the signal observations
and the sparse graph signal model. As the problem is non-convex, we propose to
solve it by alternating between a signal sparse coding and a graph update step.
We provide experimental results that outline the good graph recovery
performance of our method, which generally compares favourably to other recent
network inference algorithms
Projected Power Iteration for Network Alignment
The network alignment problem asks for the best correspondence between two
given graphs, so that the largest possible number of edges are matched. This
problem appears in many scientific problems (like the study of protein-protein
interactions) and it is very closely related to the quadratic assignment
problem which has graph isomorphism, traveling salesman and minimum bisection
problems as particular cases. The graph matching problem is NP-hard in general.
However, under some restrictive models for the graphs, algorithms can
approximate the alignment efficiently. In that spirit the recent work by Feizi
and collaborators introduce EigenAlign, a fast spectral method with convergence
guarantees for Erd\H{o}s-Reny\'i graphs. In this work we propose the algorithm
Projected Power Alignment, which is a projected power iteration version of
EigenAlign. We numerically show it improves the recovery rates of EigenAlign
and we describe the theory that may be used to provide performance guarantees
for Projected Power Alignment.Comment: 8 page
Consistency of spectral clustering in stochastic block models
We analyze the performance of spectral clustering for community extraction in
stochastic block models. We show that, under mild conditions, spectral
clustering applied to the adjacency matrix of the network can consistently
recover hidden communities even when the order of the maximum expected degree
is as small as , with the number of nodes. This result applies to
some popular polynomial time spectral clustering algorithms and is further
extended to degree corrected stochastic block models using a spherical
-median spectral clustering method. A key component of our analysis is a
combinatorial bound on the spectrum of binary random matrices, which is sharper
than the conventional matrix Bernstein inequality and may be of independent
interest.Comment: Published in at http://dx.doi.org/10.1214/14-AOS1274 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
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